[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-harmony-format-powers-gpt-oss-prompting-like-respo-summary":3,"summaries-facets-categories":489,"summary-related-harmony-format-powers-gpt-oss-prompting-like-respo-summary":4787},{"id":4,"title":5,"ai":6,"body":13,"categories":446,"created_at":447,"date_modified":447,"description":437,"extension":448,"faq":447,"featured":449,"kicker_label":447,"meta":450,"navigation":472,"path":473,"published_at":447,"question":447,"scraped_at":474,"seo":475,"sitemap":476,"source_id":477,"source_name":478,"source_type":479,"source_url":480,"stem":481,"tags":482,"thumbnail_url":447,"tldr":486,"unknown_tags":487,"__hash__":488},"summaries\u002Fsummaries\u002Fharmony-format-powers-gpt-oss-prompting-like-respo-summary.md","Harmony Format Powers gpt-oss Prompting Like Responses API",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",9224,2485,13757,0.00279645,{"type":14,"value":15,"toc":436},"minimark",[16,21,58,75,140,143,147,168,173,184,193,197,228,231,268,285,288,293,300,304,334,341,347,351,360,370,374],[17,18,20],"h2",{"id":19},"roles-establish-instruction-hierarchy-and-message-types","Roles Establish Instruction Hierarchy and Message Types",[22,23,24,25,29,30,29,33,29,36,29,39,42,43,45,46,48,49,51,52,54,55,57],"p",{},"gpt-oss models process messages via five roles forming a strict hierarchy: ",[26,27,28],"code",{},"system"," > ",[26,31,32],{},"developer",[26,34,35],{},"user",[26,37,38],{},"assistant",[26,40,41],{},"tool",". This resolves conflicts by prioritizing higher roles. ",[26,44,28],{}," sets reasoning effort, knowledge cutoff, and built-in tools. ",[26,47,32],{}," delivers core instructions (traditional system prompt) and function tools. ",[26,50,35],{}," captures inputs. ",[26,53,38],{}," outputs responses, tool calls, or reasoning, often tied to channels. ",[26,56,41],{}," feeds back results, using the tool name as the role.",[59,60,61],"blockquote",{},[22,62,63,64,29,66,29,68,29,70,29,72,74],{},"\"These roles also represent the information hierarchy that the model applies in case there are any instruction conflicts: ",[26,65,28],{},[26,67,32],{},[26,69,35],{},[26,71,38],{},[26,73,41],{},"\"",[76,77,78,91],"table",{},[79,80,81],"thead",{},[82,83,84,88],"tr",{},[85,86,87],"th",{},"Role",[85,89,90],{},"Purpose",[92,93,94,104,113,122,131],"tbody",{},[82,95,96,101],{},[97,98,99],"td",{},[26,100,28],{},[97,102,103],{},"Reasoning effort, meta info like knowledge cutoff, built-in tools",[82,105,106,110],{},[97,107,108],{},[26,109,32],{},[97,111,112],{},"Instructions and function tools",[82,114,115,119],{},[97,116,117],{},[26,118,35],{},[97,120,121],{},"Model input",[82,123,124,128],{},[97,125,126],{},[26,127,38],{},[97,129,130],{},"Tool calls or messages, channel-specific",[82,132,133,137],{},[97,134,135],{},[26,136,41],{},[97,138,139],{},"Tool outputs",[22,141,142],{},"This setup ensures models follow developer intent over user queries, critical for reliable agentic flows.",[17,144,146],{"id":145},"channels-separate-user-output-from-internal-reasoning","Channels Separate User Output from Internal Reasoning",[22,148,149,150,153,154,157,158,161,162,164,165,167],{},"Assistant messages route to three channels: ",[26,151,152],{},"final"," for end-user responses, ",[26,155,156],{},"analysis"," for chain-of-thought reasoning (unsafe for users), and ",[26,159,160],{},"commentary"," for function tool calls or preambles. Built-in tools favor ",[26,163,156],{},"; custom functions use ",[26,166,160],{},". Channels prevent leaking internal thoughts to users.",[59,169,170],{},[22,171,172],{},"\"Messages in the analysis channel do not adhere to the same safety standards as final messages do. Avoid showing these to end-users.\"",[59,174,175],{},[22,176,177,178,180,181,183],{},"\"Any function tool call will typically be triggered on the ",[26,179,160],{}," channel while built-in tools will normally be triggered on the ",[26,182,156],{}," channel.\"",[22,185,186,187,189,190,192],{},"Channels mimic Responses API separation, enabling safe streaming of ",[26,188,152],{}," content while hiding ",[26,191,156],{}," traces that boost reasoning but risk hallucinations or unsafe content.",[17,194,196],{"id":195},"harmony-renderer-library-handles-tokenization-and-parsing","Harmony Renderer Library Handles Tokenization and Parsing",[22,198,199,200,203,204,207,208,211,212,215,216,219,220,223,224,227],{},"The ",[26,201,202],{},"openai-harmony"," library (PyPI\u002Fcrates.io) automates formatting messages into tokens using ",[26,205,206],{},"o200k_harmony"," encoding (tiktoken-compatible). Construct ",[26,209,210],{},"SystemContent",", ",[26,213,214],{},"DeveloperContent"," with ",[26,217,218],{},"ToolDescription","s, and ",[26,221,222],{},"Conversation"," from ",[26,225,226],{},"Message","s, then render for completion.",[22,229,230],{},"Key workflow:",[232,233,234,241,247,253,256,262],"ol",{},[235,236,237,238],"li",{},"Load encoding: ",[26,239,240],{},"encoding = load_harmony_encoding(HarmonyEncodingName.HARMONY_GPT_OSS)",[235,242,243,244],{},"Build system: ",[26,245,246],{},"SystemContent.new().with_reasoning_effort(ReasoningEffort.HIGH).with_conversation_start_date(\"2025-06-28\")",[235,248,249,250],{},"Add developer instructions\u002Ftools: ",[26,251,252],{},"DeveloperContent.new().with_instructions(\"Always respond in riddles\").with_function_tools([ToolDescription.new(\"get_current_weather\", params_schema)])",[235,254,255],{},"Assemble conversation with user\u002Fassistant\u002Ftool messages, assign channels\u002Frecipients\u002Fcontent types.",[235,257,258,259],{},"Render: ",[26,260,261],{},"tokens = encoding.render_conversation_for_completion(convo, Role.ASSISTANT)",[235,263,264,265],{},"Parse response: ",[26,266,267],{},"parsed_response = encoding.parse_messages_from_completion_tokens(new_tokens, Role.ASSISTANT)",[22,269,270,271,274,275,211,278,211,281,284],{},"For streaming, ",[26,272,273],{},"StreamableParser"," decodes tokens incrementally, exposing ",[26,276,277],{},"current_role",[26,279,280],{},"current_channel",[26,282,283],{},"last_content_delta",", etc., ideal for real-time UIs handling JSON or unicode.",[22,286,287],{},"Example stream output tracks shifts: analysis reasoning → commentary tool call → final response.",[59,289,290],{},[22,291,292],{},"\"gpt-oss should not be used without using the harmony format, as it will not work correctly.\"",[22,294,295,296,299],{},"This library abstracts special tokens like ",[26,297,298],{},"\u003C|type|>",", ensuring compatibility without manual prompt engineering.",[17,301,303],{"id":302},"custom-renderers-must-mimic-responses-api-with-special-tokens","Custom Renderers Must Mimic Responses API with Special Tokens",[22,305,306,307,309,310,211,313,316,317,211,320,211,323,326,327,211,330,333],{},"For self-built inference (e.g., Ollama bypass), replicate Harmony using ",[26,308,206],{}," encoding. Special tokens structure inputs: ",[26,311,312],{},"\u003C|system|>",[26,314,315],{},"\u003C|developer|>",", etc., with channels via ",[26,318,319],{},"\u003C|final|>",[26,321,322],{},"\u003C|analysis|>",[26,324,325],{},"\u003C|commentary|>",". Tool calls specify ",[26,328,329],{},"\u003C|recipient|functions.tool_name|>",[26,331,332],{},"\u003C|constrain|>json>",". Preambles in commentary precede multi-calls.",[22,335,336,337,340],{},"Format emulates Responses API familiarity: conversation history → assistant completion. Include ",[26,338,339],{},"ReasoningEffort"," (LOW\u002FMED\u002FHIGH) in system for compute trade-offs. Conversation start date aids recency awareness.",[22,342,343,344,346],{},"Without the library, manually tokenize messages respecting hierarchy and channels—error-prone, hence the recommendation to use ",[26,345,202],{},".",[17,348,350],{"id":349},"production-implications-safety-streaming-and-model-limits","Production Implications: Safety, Streaming, and Model Limits",[22,352,353,354,356,357,359],{},"Harmony enforces safety by isolating ",[26,355,156],{}," (weaker safeguards) from ",[26,358,152],{},". High reasoning effort trades latency for accuracy in complex tasks. For APIs\u002Fproviders, inference handles formatting; direct gpt-oss needs explicit Harmony. Avoid raw gpt-oss sans format—degrades to incoherent outputs.",[22,361,362,363,365,366,369],{},"Integrates with function calling: JSON schemas in ",[26,364,218],{},", results as ",[26,367,368],{},"Author(Role.TOOL, tool_name)",". Streams parse mid-generation for low-latency apps.",[17,371,373],{"id":372},"key-takeaways","Key Takeaways",[375,376,377,380,386,398,404,415,421,424,430],"ul",{},[235,378,379],{},"Always pair gpt-oss with Harmony format; skip it and models fail on structured tasks.",[235,381,382,383,385],{},"Prioritize ",[26,384,28],{}," for reasoning effort (HIGH for agents) and date cutoffs to ground responses.",[235,387,388,389,391,392,394,395,397],{},"Route assistant outputs: ",[26,390,152],{}," to users, ",[26,393,156],{}," internally, ",[26,396,160],{}," for tools.",[235,399,400,401,403],{},"Install ",[26,402,202],{}," via PyPI\u002Fcrates.io—renders conversations to tokens, parses streams.",[235,405,406,407,409,410,412,413,346],{},"Define tools in ",[26,408,32],{}," role with JSON schemas; echo results as ",[26,411,41],{}," role in ",[26,414,160],{},[235,416,417,418,420],{},"Use ",[26,419,273],{}," for real-time decoding: track channels\u002Fdeltas without full tokens.",[235,422,423],{},"Leverage role hierarchy to override user instructions reliably.",[235,425,426,427,429],{},"Test with ",[26,428,206],{}," encoding in tiktoken for custom setups.",[235,431,432,433,435],{},"Hide ",[26,434,156],{}," channel from users—lacks full safety filters.",{"title":437,"searchDepth":438,"depth":438,"links":439},"",2,[440,441,442,443,444,445],{"id":19,"depth":438,"text":20},{"id":145,"depth":438,"text":146},{"id":195,"depth":438,"text":196},{"id":302,"depth":438,"text":303},{"id":349,"depth":438,"text":350},{"id":372,"depth":438,"text":373},[],null,"md",false,{"content_references":451,"triage":467},[452,456,459,461,464],{"type":41,"title":453,"url":454,"context":455},"gpt-oss models","https:\u002F\u002Fopenai.com\u002Fopen-models","mentioned",{"type":41,"title":202,"url":457,"context":458},"https:\u002F\u002Fpypi.org\u002Fproject\u002Fopenai-harmony\u002F","recommended",{"type":41,"title":202,"url":460,"context":458},"https:\u002F\u002Fcrates.io\u002Fcrates\u002Fopenai-harmony",{"type":41,"title":462,"url":463,"context":455},"tiktoken","https:\u002F\u002Fgithub.com\u002Fopenai\u002Ftiktoken",{"type":465,"title":466,"context":455},"other","OpenAI Responses API",{"relevance":468,"novelty":469,"quality":469,"actionability":469,"composite":470,"reasoning":471},5,4,4.35,"Category: AI & LLMs. The article provides a detailed explanation of the Harmony response format and its application in gpt-oss models, addressing specific pain points for developers integrating AI features. It offers actionable insights on structuring prompts and managing message types, which are crucial for building reliable AI-powered products.",true,"\u002Fsummaries\u002Fharmony-format-powers-gpt-oss-prompting-like-respo-summary","2026-04-16 03:07:30",{"title":5,"description":437},{"loc":473},"7927c9dc29552c98","__oneoff__","article","https:\u002F\u002Fcookbook.openai.com\u002Farticles\u002Fopenai-harmony","summaries\u002Fharmony-format-powers-gpt-oss-prompting-like-respo-summary",[483,484,485],"llm","prompt-engineering","gpt-oss","gpt-oss models demand the Harmony response format for conversations, reasoning traces, and tool calls—use dedicated roles, channels, and the openai-harmony library to mimic OpenAI's Responses API without custom inference 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LLM Outputs Match Exact Taxonomies with Tries",{"provider":7,"model":8,"input_tokens":4792,"output_tokens":4793,"processing_time_ms":4794,"cost_usd":4795},7679,2345,26271,0.0026858,{"type":14,"value":4797,"toc":5139},[4798,4802,4805,4811,4814,4837,4841,4844,5016,5023,5030,5105,5112,5116,5123,5126,5129,5132,5135],[17,4799,4801],{"id":4800},"logit-masking-guarantees-valid-outputs","Logit Masking Guarantees Valid Outputs",[22,4803,4804],{},"LLMs generate tokens autoregressively, producing a logit vector over 32,000-100,000 vocabulary tokens at each step, converted to probabilities via softmax. Any token with finite logit has nonzero probability, allowing hallucinations like near-miss labels (e.g., \"Techology\" instead of \"Technology\"). Standard fixes—prompt instructions, string matching, retries—fail because they act post-generation.",[22,4806,4807,4808,346],{},"Constrained decoding intervenes pre-sampling: set logits of invalid tokens to -∞, yielding exactly zero softmax probability. Remaining valid logits renormalize to sum to 1. This works for any sampling (greedy, temperature, top-p, top-k) since zero-probability tokens cannot be selected. In code: ",[26,4809,4810],{},"logits[~valid_token_mask] = float('-inf')",[22,4812,4813],{},"Validity depends on prior tokens. A trie (prefix tree) encodes all taxonomy labels as token paths. Root children are first tokens of any label; deeper nodes narrow to continuations. After prefix \" Tech\" (token ID 8987), only \"nology\" (ID 1366) is valid. At end nodes, only EOS is valid, terminating the label.",[22,4815,4816,4817,211,4820,4823,4824,4828,4829,4832,4833,4836],{},"Tokenization nuance: BPE splits depend on context. Tokenize labels as continuations with leading space (",[26,4818,4819],{},"\" \" + label",[26,4821,4822],{},"add_special_tokens=False","), e.g., Qwen2.5 tokenizes \" Sports\" to ",[4825,4826,4827],"span",{},"22470",", not \"Sports\" to ",[4825,4830,4831],{},"51660",". Verify round-trip: ",[26,4834,4835],{},"tokenizer.decode(token_ids) == \" \" + label",". Tiktoken (GPT-4 family) bakes whitespace into boundaries without ▁.",[17,4838,4840],{"id":4839},"trie-and-logits-processor-implementation","Trie and Logits Processor Implementation",[22,4842,4843],{},"Build trie from labels:",[4845,4846,4850],"pre",{"className":4847,"code":4848,"language":4849,"meta":437,"style":437},"language-python shiki shiki-themes github-light github-dark","class TrieNode:\n    def __init__(self):\n        self.children = {}  # token_id → TrieNode\n        self.is_end = False\n\nclass ConstrainedTrie:\n    def __init__(self):\n        self.root = TrieNode()\n    def insert(self, token_ids):\n        node = self.root\n        for tid in token_ids:\n            if tid not in node.children:\n                node.children[tid] = TrieNode()\n            node = node.children[tid]\n        node.is_end = True\n    def get_valid_next_tokens(self, prefix):\n        node = self.root\n        for tid in prefix:\n            if tid not in node.children:\n                return set()\n            node = node.children[tid]\n        return set(node.children.keys())\n    def is_complete(self, prefix):\n        node = self.root\n        for tid in prefix:\n            if tid not in node.children:\n                return False\n            node = node.children[tid]\n        return node.is_end\n","python",[26,4851,4852,4859,4864,4870,4875,4880,4886,4891,4897,4903,4909,4915,4921,4927,4933,4939,4945,4950,4956,4961,4967,4972,4978,4984,4989,4994,4999,5005,5010],{"__ignoreMap":437},[4825,4853,4856],{"class":4854,"line":4855},"line",1,[4825,4857,4858],{},"class TrieNode:\n",[4825,4860,4861],{"class":4854,"line":438},[4825,4862,4863],{},"    def __init__(self):\n",[4825,4865,4867],{"class":4854,"line":4866},3,[4825,4868,4869],{},"        self.children = {}  # token_id → TrieNode\n",[4825,4871,4872],{"class":4854,"line":469},[4825,4873,4874],{},"        self.is_end = False\n",[4825,4876,4877],{"class":4854,"line":468},[4825,4878,4879],{"emptyLinePlaceholder":472},"\n",[4825,4881,4883],{"class":4854,"line":4882},6,[4825,4884,4885],{},"class ConstrainedTrie:\n",[4825,4887,4889],{"class":4854,"line":4888},7,[4825,4890,4863],{},[4825,4892,4894],{"class":4854,"line":4893},8,[4825,4895,4896],{},"        self.root = TrieNode()\n",[4825,4898,4900],{"class":4854,"line":4899},9,[4825,4901,4902],{},"    def insert(self, token_ids):\n",[4825,4904,4906],{"class":4854,"line":4905},10,[4825,4907,4908],{},"        node = self.root\n",[4825,4910,4912],{"class":4854,"line":4911},11,[4825,4913,4914],{},"        for tid in token_ids:\n",[4825,4916,4918],{"class":4854,"line":4917},12,[4825,4919,4920],{},"            if tid not in node.children:\n",[4825,4922,4924],{"class":4854,"line":4923},13,[4825,4925,4926],{},"                node.children[tid] = TrieNode()\n",[4825,4928,4930],{"class":4854,"line":4929},14,[4825,4931,4932],{},"            node = node.children[tid]\n",[4825,4934,4936],{"class":4854,"line":4935},15,[4825,4937,4938],{},"        node.is_end = True\n",[4825,4940,4942],{"class":4854,"line":4941},16,[4825,4943,4944],{},"    def get_valid_next_tokens(self, prefix):\n",[4825,4946,4948],{"class":4854,"line":4947},17,[4825,4949,4908],{},[4825,4951,4953],{"class":4854,"line":4952},18,[4825,4954,4955],{},"        for tid in prefix:\n",[4825,4957,4959],{"class":4854,"line":4958},19,[4825,4960,4920],{},[4825,4962,4964],{"class":4854,"line":4963},20,[4825,4965,4966],{},"                return set()\n",[4825,4968,4970],{"class":4854,"line":4969},21,[4825,4971,4932],{},[4825,4973,4975],{"class":4854,"line":4974},22,[4825,4976,4977],{},"        return set(node.children.keys())\n",[4825,4979,4981],{"class":4854,"line":4980},23,[4825,4982,4983],{},"    def is_complete(self, prefix):\n",[4825,4985,4987],{"class":4854,"line":4986},24,[4825,4988,4908],{},[4825,4990,4992],{"class":4854,"line":4991},25,[4825,4993,4955],{},[4825,4995,4997],{"class":4854,"line":4996},26,[4825,4998,4920],{},[4825,5000,5002],{"class":4854,"line":5001},27,[4825,5003,5004],{},"                return False\n",[4825,5006,5008],{"class":4854,"line":5007},28,[4825,5009,4932],{},[4825,5011,5013],{"class":4854,"line":5012},29,[4825,5014,5015],{},"        return node.is_end\n",[22,5017,5018,5019,5022],{},"Insert: ",[26,5020,5021],{},"token_ids = tokenizer.encode(\" \" + label, add_special_tokens=False); trie.insert(token_ids)",". Rebuild on taxonomy changes (milliseconds for hundreds-thousands labels).",[22,5024,5025,5026,5029],{},"HuggingFace ",[26,5027,5028],{},"LogitsProcessor",":",[4845,5031,5033],{"className":4847,"code":5032,"language":4849,"meta":437,"style":437},"class TrieLogitsProcessor(LogitsProcessor):\n    def __init__(self, trie, prompt_length, eos_token_id):\n        self.trie = trie\n        self.prompt_length = prompt_length\n        self.eos = eos_token_id\n    def __call__(self, input_ids, scores):\n        generated = input_ids[0, self.prompt_length:].tolist()\n        valid = self.trie.get_valid_next_tokens(generated)\n        if self.trie.is_complete(generated):\n            valid.add(self.eos)\n        masked = torch.full_like(scores, float('-inf'))\n        for tid in valid:\n            masked[0, tid] = scores[0, tid]\n        return masked\n",[26,5034,5035,5040,5045,5050,5055,5060,5065,5070,5075,5080,5085,5090,5095,5100],{"__ignoreMap":437},[4825,5036,5037],{"class":4854,"line":4855},[4825,5038,5039],{},"class TrieLogitsProcessor(LogitsProcessor):\n",[4825,5041,5042],{"class":4854,"line":438},[4825,5043,5044],{},"    def __init__(self, trie, prompt_length, eos_token_id):\n",[4825,5046,5047],{"class":4854,"line":4866},[4825,5048,5049],{},"        self.trie = trie\n",[4825,5051,5052],{"class":4854,"line":469},[4825,5053,5054],{},"        self.prompt_length = prompt_length\n",[4825,5056,5057],{"class":4854,"line":468},[4825,5058,5059],{},"        self.eos = eos_token_id\n",[4825,5061,5062],{"class":4854,"line":4882},[4825,5063,5064],{},"    def __call__(self, input_ids, scores):\n",[4825,5066,5067],{"class":4854,"line":4888},[4825,5068,5069],{},"        generated = input_ids[0, self.prompt_length:].tolist()\n",[4825,5071,5072],{"class":4854,"line":4893},[4825,5073,5074],{},"        valid = self.trie.get_valid_next_tokens(generated)\n",[4825,5076,5077],{"class":4854,"line":4899},[4825,5078,5079],{},"        if self.trie.is_complete(generated):\n",[4825,5081,5082],{"class":4854,"line":4905},[4825,5083,5084],{},"            valid.add(self.eos)\n",[4825,5086,5087],{"class":4854,"line":4911},[4825,5088,5089],{},"        masked = torch.full_like(scores, float('-inf'))\n",[4825,5091,5092],{"class":4854,"line":4917},[4825,5093,5094],{},"        for tid in valid:\n",[4825,5096,5097],{"class":4854,"line":4923},[4825,5098,5099],{},"            masked[0, tid] = scores[0, tid]\n",[4825,5101,5102],{"class":4854,"line":4929},[4825,5103,5104],{},"        return masked\n",[22,5106,5107,5108,5111],{},"Generate: ",[26,5109,5110],{},"model.generate(input_ids, logits_processor=LogitsProcessorList([processor]), max_new_tokens=16)",". Output decodes to exact label.",[17,5113,5115],{"id":5114},"multi-label-hierarchies-and-broader-applications","Multi-Label, Hierarchies, and Broader Applications",[22,5117,5118,5119,5122],{},"For multi-label: After end node, allow EOS or separator (e.g., ",[26,5120,5121],{},"|,|","). Parse generated tokens into seen labels and current prefix. At root, exclude first tokens only after all labels sharing it are emitted (precompute groups by first token). Supports hierarchies: insert full paths like \"Technology > AI > NLP\"; shared prefixes compress naturally.",[22,5124,5125],{},"Edge cases: Low confidence concentrates mass on valid tokens (fix: fine-tune); long labels create narrow paths (fine-tune improves); rebuild trie on changes.",[22,5127,5128],{},"Proof of correctness: (1) Forward invariant—emitted tokens always extend valid prefixes; (2) Termination invariant—EOS only at end nodes. Verify by enumerating trie paths against labels. Independent of model, temperature, etc.",[22,5130,5131],{},"Limitations: Needs logit access (open models like Qwen2.5, not OpenAI APIs); masking redistributes probability (structurally correct but semantically wrong possible); no accuracy boost—pair with fine-tuning.",[22,5133,5134],{},"Generalizes to JSON (trie encodes schema), SQL (grammar FSM), agents (tool names). Enforces structure without prompt\u002Fmodel changes.",[5136,5137,5138],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":437,"searchDepth":438,"depth":438,"links":5140},[5141,5142,5143],{"id":4800,"depth":438,"text":4801},{"id":4839,"depth":438,"text":4840},{"id":5114,"depth":438,"text":5115},[526],{"content_references":5146,"triage":5155},[5147,5150],{"type":41,"title":5148,"url":5149,"context":458},"constrained-decoding","https:\u002F\u002Fgithub.com\u002FSachinKalsi\u002Fconstrained-decoding",{"type":465,"title":5151,"author":5152,"url":5153,"context":5154},"Why do we use negative infinity for masking in attention","Sachin Kalsi","https:\u002F\u002Fmedium.com\u002F@sachinkalsi\u002Fwhy-do-we-use-negative-infinity-for-masking-in-attention-450c59274ac8","cited",{"relevance":468,"novelty":469,"quality":469,"actionability":469,"composite":470,"reasoning":5156},"Category: AI & LLMs. The article provides a detailed method for constraining LLM outputs to match specific taxonomies, addressing a key pain point for developers integrating AI features. It includes practical code examples and a clear explanation of the trie data structure, making it actionable for the audience.","\u002Fsummaries\u002Fguarantee-llm-outputs-match-exact-taxonomies-with-summary","2026-05-07 04:37:46","2026-05-07 11:23:51",{"title":4790,"description":437},{"loc":5157},"b0d82d6ef098f216","Towards AI","https:\u002F\u002Fpub.towardsai.net\u002Fconstrained-decoding-forcing-llms-to-respect-your-taxonomy-3aaaf13329f9?source=rss----98111c9905da---4","summaries\u002Fguarantee-llm-outputs-match-exact-taxonomies-with--summary",[483,484],"Constrain LLM generation by masking invalid logits to -∞ using a trie of tokenized labels, ensuring outputs are always exact taxonomy matches regardless of sampling method.",[],"9GhOaZxleWT_PuR2iUmqtZjKiZBztn3E_c7SBSQ3cAc",{"id":5171,"title":5172,"ai":5173,"body":5178,"categories":5280,"created_at":447,"date_modified":447,"description":437,"extension":448,"faq":447,"featured":449,"kicker_label":447,"meta":5281,"navigation":472,"path":5299,"published_at":5300,"question":447,"scraped_at":5301,"seo":5302,"sitemap":5303,"source_id":5304,"source_name":5305,"source_type":479,"source_url":5306,"stem":5307,"tags":5308,"thumbnail_url":447,"tldr":5309,"unknown_tags":5310,"__hash__":5311},"summaries\u002Fsummaries\u002Frebuild-gpt-5-5-prompts-from-scratch-minimal-wins--summary.md","Rebuild GPT-5.5 Prompts from Scratch: Minimal Wins Over Legacy Detail",{"provider":7,"model":8,"input_tokens":5174,"output_tokens":5175,"processing_time_ms":5176,"cost_usd":5177},5455,1761,19749,0.00194885,{"type":14,"value":5179,"toc":5275},[5180,5184,5187,5190,5193,5196,5200,5203,5247,5250,5253,5256,5259,5263,5266,5269,5272],[17,5181,5183],{"id":5182},"strip-prompts-to-outcomes-for-better-reasoning-efficiency","Strip Prompts to Outcomes for Better Reasoning Efficiency",[22,5185,5186],{},"GPT-5.5 outperforms predecessors by reasoning more efficiently, so legacy prompts with step-by-step instructions create noise, narrow search space, or yield mechanical outputs. Instead, define only the target outcome, success criteria, constraints, and context—let the model handle the process. Test low or medium reasoning effort first; short prompts beat process-heavy stacks.",[22,5188,5189],{},"Avoid absolutes like \"ALWAYS\" or \"NEVER\" except for invariants (e.g., security). Use decision rules for judgment calls and explicit stop conditions to prevent tool loops: \"Resolve in fewest useful loops without sacrificing correctness; after each result, check if core request is answerable with evidence.\"",[22,5191,5192],{},"Positive example for customer service: \"Resolve issue end-to-end. Success: eligibility from policy\u002Faccount data, complete actions before responding, output includes completed_actions, customer_message, blockers; ask for smallest missing field if needed.\" Negative: micromanaging \"First inspect A, then B, compare fields, think exceptions, decide tool...\"",[22,5194,5195],{},"This approach unlocks higher performance by giving GPT-5.5 room to optimize paths, reducing latency and improving naturalness.",[17,5197,5199],{"id":5198},"use-7-part-schema-starting-with-role-and-personality","Use 7-Part Schema Starting with Role and Personality",[22,5201,5202],{},"Structure prompts as:",[375,5204,5205,5211,5217,5223,5229,5235,5241],{},[235,5206,5207,5210],{},[5208,5209,87],"strong",{},": 1-2 sentences on function, context, job.",[235,5212,5213,5216],{},[5208,5214,5215],{},"# Personality",": Tone, demeanor, collaboration style.",[235,5218,5219,5222],{},[5208,5220,5221],{},"# Goal",": User-visible outcome.",[235,5224,5225,5228],{},[5208,5226,5227],{},"# Success criteria",": What must be true before final answer.",[235,5230,5231,5234],{},[5208,5232,5233],{},"# Constraints",": Policy, safety, evidence limits.",[235,5236,5237,5240],{},[5208,5238,5239],{},"# Output",": Sections, length, tone.",[235,5242,5243,5246],{},[5208,5244,5245],{},"# Stop rules",": When to retry, fallback, abstain, ask, stop.",[22,5248,5249],{},"Role definitions counter prior doubts (e.g., some research called them counterproductive); they now anchor effective prompts. Split personality (sound: warm\u002Fformal) from collaboration (ask questions\u002Fassume when clear).",[22,5251,5252],{},"Task-focused: \"Capable collaborator: approachable, steady, direct. Assume competence\u002Fgood faith; progress over clarification unless material risk.\"",[22,5254,5255],{},"Expressive: \"Vivid presence: intelligent, curious, playful. Ask on blurriness, decisive with context; warm, offer viewpoint without mirroring.\"",[22,5257,5258],{},"Keep sections short—add details only if they shift behavior. Treat as starting point, tune with examples.",[17,5260,5262],{"id":5261},"set-retrieval-budgets-citation-rules-and-streaming-preambles","Set Retrieval Budgets, Citation Rules, and Streaming Preambles",[22,5264,5265],{},"Embed citation logic in prompts: specify claims needing evidence (e.g., metrics, dates), sufficient proof, and responses to gaps. Retrieval budgets as stop rules: one broad search first; retry only if core unanswerable, facts missing, exhaustive needed, or specific docs required. Skip for phrasing\u002Fexamples.",[22,5267,5268],{},"Drafting rule: Cite product\u002Fmetrics claims; avoid inventing specifics—use generics\u002Fplaceholders if unsupported.",[22,5270,5271],{},"For streaming, cut perceived latency with preambles: Before tools, send 1-2 sentences acknowledging request and first step (e.g., for multi-step tasks).",[22,5273,5274],{},"Automate rewrites via Codex or OpenAI's Docs Skill GitHub tool.",{"title":437,"searchDepth":438,"depth":438,"links":5276},[5277,5278,5279],{"id":5182,"depth":438,"text":5183},{"id":5198,"depth":438,"text":5199},{"id":5261,"depth":438,"text":5262},[],{"content_references":5282,"triage":5296},[5283,5286,5289,5293],{"type":465,"title":5284,"url":5285,"context":5154},"prompting guide for GPT-5.5","https:\u002F\u002Fdevelopers.openai.com\u002Fapi\u002Fdocs\u002Fguides\u002Fprompt-guidance?model=gpt-5.5",{"type":465,"title":5287,"url":5288,"context":455},"General Tips","https:\u002F\u002Fdevelopers.openai.com\u002Fapi\u002Fdocs\u002Fguides\u002Flatest-model",{"type":5290,"title":5291,"url":5292,"context":455},"paper","arxiv.org\u002Fabs\u002F2603.18507","https:\u002F\u002Farxiv.org\u002Fabs\u002F2603.18507",{"type":41,"title":5294,"url":5295,"context":458},"OpenAI Docs Skill","https:\u002F\u002Fgithub.com\u002Fopenai\u002Fskills\u002Ftree\u002Fmain\u002Fskills\u002F.curated\u002Fopenai-docs",{"relevance":468,"novelty":469,"quality":469,"actionability":468,"composite":5297,"reasoning":5298},4.55,"Category: AI & LLMs. The article provides a detailed framework for prompt engineering specifically for GPT-5.5, addressing the pain point of outdated prompt structures that limit AI performance. It offers a clear, actionable 7-part schema that developers can implement immediately to enhance their AI interactions.","\u002Fsummaries\u002Frebuild-gpt-5-5-prompts-from-scratch-minimal-wins-summary","2026-04-26 10:20:04","2026-04-26 17:22:51",{"title":5172,"description":437},{"loc":5299},"568ea01dbb8e8f83","The Decoder","https:\u002F\u002Fthe-decoder.com\u002Fopenai-says-old-prompts-are-holding-gpt-5-5-back-and-developers-need-a-fresh-baseline\u002F","summaries\u002Frebuild-gpt-5-5-prompts-from-scratch-minimal-wins--summary",[484,483],"OpenAI's GPT-5.5 guide: Ditch old detailed prompts—they limit performance. Start with minimal, outcome-focused instructions in a 7-part schema beginning with role definitions to leverage efficient reasoning.",[],"vr6FYF1C8Y09vn5ZuuTzZgt9eRUSHktnfYZnS8xYRuI",{"id":5313,"title":5314,"ai":5315,"body":5320,"categories":5349,"created_at":447,"date_modified":447,"description":437,"extension":448,"faq":447,"featured":449,"kicker_label":447,"meta":5350,"navigation":472,"path":5358,"published_at":5359,"question":447,"scraped_at":5360,"seo":5361,"sitemap":5362,"source_id":5363,"source_name":5364,"source_type":479,"source_url":5365,"stem":5366,"tags":5367,"thumbnail_url":447,"tldr":5368,"unknown_tags":5369,"__hash__":5370},"summaries\u002Fsummaries\u002Fkernel-framework-delivers-340-ai-accuracy-gains-summary.md","KERNEL Framework Delivers 340% AI Accuracy Gains",{"provider":7,"model":8,"input_tokens":5316,"output_tokens":5317,"processing_time_ms":5318,"cost_usd":5319},3871,1802,16733,0.0016527,{"type":14,"value":5321,"toc":5345},[5322,5326,5329,5332,5336,5339,5342],[17,5323,5325],{"id":5324},"overcoming-vague-prompts-with-kernel-principles","Overcoming Vague Prompts with KERNEL Principles",[22,5327,5328],{},"Long, complicated, vague prompts produce inconsistent AI outputs, as the author experienced in an enterprise IoT project where responses varied wildly. The solution is the KERNEL Framework—a practical six-principle checklist (K, E, R, N, E, L) that enforces simplicity, focus, and verifiability. This shifts prompts from frustrating guesswork to precise, reliable instructions, directly improving accuracy by up to 340% in production systems like IoT.",[22,5330,5331],{},"Use it as a go-to checklist: instead of overloading prompts with details, strip them to essentials that guide the AI clearly. The framework turns theoretical prompt engineering into a repeatable process, eliminating output chaos without needing advanced skills.",[17,5333,5335],{"id":5334},"proven-results-from-hands-on-application","Proven Results from Hands-On Application",[22,5337,5338],{},"In real-world testing, KERNEL transformed unreliable LLM responses into consistent, high-quality ones. The author credits it for clarity in complex environments, where vague prompts fail but structured ones succeed. Key outcome: prompts become easy to verify and iterate, reducing trial-and-error cycles.",[22,5340,5341],{},"Trade-off: it prioritizes precision over verbosity, so avoid it for creative brainstorming—reserve for accuracy-critical tasks like data analysis or system integration. Readers overwhelmed by varying AI results gain a immediate tool: apply the six principles before every prompt to see measurable reliability gains.",[22,5343,5344],{},"This content teases the framework effectively but is thin on specifics, as full details are paywalled.",{"title":437,"searchDepth":438,"depth":438,"links":5346},[5347,5348],{"id":5324,"depth":438,"text":5325},{"id":5334,"depth":438,"text":5335},[526],{"content_references":5351,"triage":5355},[5352],{"type":465,"title":5353,"url":5354,"context":455},"KERNEL Framework","https:\u002F\u002Fwww.shailykumar.com\u002Fprompt-engineering-mastery",{"relevance":468,"novelty":469,"quality":4866,"actionability":469,"composite":5356,"reasoning":5357},4.1,"Category: AI & LLMs. The article maps directly to the AI & LLMs category by discussing the KERNEL Framework for prompt engineering, which addresses a specific pain point of producing consistent AI outputs. It provides actionable principles for improving prompt accuracy, making it relevant for developers looking to implement AI features.","\u002Fsummaries\u002Fkernel-framework-delivers-340-ai-accuracy-gains-summary","2026-04-26 03:11:01","2026-04-26 17:22:34",{"title":5314,"description":437},{"loc":5358},"d40c4d615fa22540","AI Simplified in Plain English","https:\u002F\u002Fmedium.com\u002Fai-simplified-in-plain-english\u002F340-higher-ai-accuracy-d4d0fec20b28?source=rss----f37ab7d4e76b---4","summaries\u002Fkernel-framework-delivers-340-ai-accuracy-gains-summary",[484,483],"Apply the KERNEL Framework's six principles to craft simple, focused, verifiable prompts that boost AI accuracy up to 340%, as proven in enterprise IoT projects.",[],"5SDYvUIRgP1B_fN1EtmZOD5q5r2qhHtDCvQvGtsU6Jc",{"id":5372,"title":5373,"ai":5374,"body":5379,"categories":5431,"created_at":447,"date_modified":447,"description":437,"extension":448,"faq":447,"featured":449,"kicker_label":447,"meta":5432,"navigation":472,"path":5433,"published_at":5434,"question":447,"scraped_at":447,"seo":5435,"sitemap":5436,"source_id":5437,"source_name":5438,"source_type":479,"source_url":5439,"stem":5440,"tags":5441,"thumbnail_url":447,"tldr":5442,"unknown_tags":5443,"__hash__":5444},"summaries\u002Fsummaries\u002F7-prompts-to-stop-ai-sycophancy-summary.md","7 Prompts to Stop AI Sycophancy",{"provider":7,"model":8,"input_tokens":5375,"output_tokens":5376,"processing_time_ms":5377,"cost_usd":5378},5920,1314,11672,0.0013667,{"type":14,"value":5380,"toc":5426},[5381,5385,5388,5391,5395,5398,5401,5404,5407,5410,5414,5417,5420,5423],[17,5382,5384],{"id":5383},"sycophancy-stems-from-rlhf-human-biases","Sycophancy Stems from RLHF Human Biases",[22,5386,5387],{},"Large language models become overly agreeable because reinforcement learning from human feedback (RLHF) rewards responses aligning with users' preexisting views. Humans rate flattering outputs higher, so models learn to prioritize agreement over truth. This led OpenAI to rollback a GPT-4o update that amplified insincere support. Labs like Anthropic, Google (Gemini 3), and OpenAI acknowledge the issue and are addressing it, but prompts offer immediate fixes.",[22,5389,5390],{},"Impact: Without intervention, AI provides unhelpful praise instead of constructive challenges, wasting time on flawed ideas.",[17,5392,5394],{"id":5393},"rephrase-prompts-to-demand-risks-and-specificity","Rephrase Prompts to Demand Risks and Specificity",[22,5396,5397],{},"Shift from open \"What do you think?\" to targeted criticism requests. For a premium dog-walking service, ask \"What are the biggest risks and reasons this might fail?\" instead of general opinions—this pulls brakes on blind acceptance.",[22,5399,5400],{},"Force ratings for grounded feedback: Rate a poem (\"Roses are red. Bad people are bad. So be good. As you well should.\") out of 10 with reasoning, preventing vague praise.",[22,5402,5403],{},"Present multiple options to trigger comparisons: Evaluate podcast names like \"Who’s Awake?\", \"Wake Up Call\", \"Coffee First\" to enter pros\u002Fcons mode.",[22,5405,5406],{},"Ask neutrally before sharing your view: \"Should I name my bakery ‘The Bread Place’?\" avoids anchoring bias from statements like \"I’m proud of ‘The Bread Place’ for its simplicity.\"",[22,5408,5409],{},"Impact: These elicit balanced analysis, exposing weaknesses early—e.g., AI flags poor slogans like \"Coffee and other things\" more harshly when not tied to your ego.",[17,5411,5413],{"id":5412},"control-context-and-adopt-critical-personas","Control Context and Adopt Critical Personas",[22,5415,5416],{},"Start fresh chats or use incognito\u002Ftemporary modes (ChatGPT, Claude, Gemini) to avoid history priming agreement via memory features.",[22,5418,5419],{},"Frame ideas as others': Critique \"Some guy came up with ‘Coffee and other things’\" gets blunt feedback versus your own idea.",[22,5421,5422],{},"Assign critical personas: \"You're Gordon Ramsay\" judging bacon in spaghetti bolognese enables sharp pushback without default politeness.",[22,5424,5425],{},"Impact: Removes personal flattery incentives, delivering honest critiques—e.g., harsher on third-party work, or Ramsay-style roasts that reveal real flaws.",{"title":437,"searchDepth":438,"depth":438,"links":5427},[5428,5429,5430],{"id":5383,"depth":438,"text":5384},{"id":5393,"depth":438,"text":5394},{"id":5412,"depth":438,"text":5413},[],{},"\u002Fsummaries\u002F7-prompts-to-stop-ai-sycophancy-summary","2026-04-08 21:21:17",{"title":5373,"description":437},{"loc":5433},"ee586f533efa4843","Why Try AI","https:\u002F\u002Funknown","summaries\u002F7-prompts-to-stop-ai-sycophancy-summary",[484,483],"LLMs flatter due to RLHF training on humans preferring agreement—fix it now with 7 prompt tweaks that force criticism, like asking for risks or using critical personas.",[],"Z_SdseCZEIzh_tiT5UxEwsMxYT2FrWdjk4l8NGOj0hU"]