[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-a3b9c63845c24fc6-liquid-ai-s-lfm2-5-8b-a1b-efficient-on-device-reas-summary":3,"summaries-facets-categories":115,"summary-related-a3b9c63845c24fc6-liquid-ai-s-lfm2-5-8b-a1b-efficient-on-device-reas-summary":4298},{"id":4,"title":5,"ai":6,"body":13,"categories":76,"created_at":78,"date_modified":78,"description":70,"extension":79,"faq":78,"featured":80,"kicker_label":78,"meta":81,"navigation":97,"path":98,"published_at":99,"question":78,"scraped_at":99,"seo":100,"sitemap":101,"source_id":102,"source_name":103,"source_type":104,"source_url":105,"stem":106,"tags":107,"thumbnail_url":78,"tldr":112,"tweet":78,"unknown_tags":113,"__hash__":114},"summaries\u002Fsummaries\u002Fa3b9c63845c24fc6-liquid-ai-s-lfm2-5-8b-a1b-efficient-on-device-reas-summary.md","Liquid AI's LFM2.5-8B-A1B: Efficient On-Device Reasoning",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",9924,734,3277,0.003582,{"type":14,"value":15,"toc":69},"minimark",[16,21,25,29,32,35,39],[17,18,20],"h2",{"id":19},"architecture-and-efficiency","Architecture and Efficiency",[22,23,24],"p",{},"Liquid AI's LFM2.5-8B-A1B is a sparse Mixture-of-Experts (MoE) model designed specifically for on-device deployment. It features 8.3B total parameters but activates only 1.5B per forward pass, significantly reducing compute requirements. The architecture consists of 24 layers: 18 double-gated LIV convolution blocks and 6 Grouped Query Attention (GQA) layers. This design allows the model to maintain high performance while remaining efficient enough to run on consumer hardware, achieving ~30 tokens\u002Fs on mobile devices and 253 tokens\u002Fs on an M5 Max CPU.",[17,26,28],{"id":27},"training-and-reasoning-capabilities","Training and Reasoning Capabilities",[22,30,31],{},"Unlike its predecessor, LFM2.5-8B-A1B is optimized as a reasoning-only model, requiring an explicit chain of thought before generating a final answer. Liquid AI scaled pretraining to 38T tokens and expanded the context window to 128K tokens. The model also features a doubled vocabulary size (128,000), which improves tokenization efficiency for non-Latin scripts, particularly in Hindi, Thai, Vietnamese, Indonesian, and Arabic.",[22,33,34],{},"To ensure reliability, the training process included two reinforcement learning stages: one to reduce 'doom loops' in reasoning traces and another using an avg@k-based reward to minimize hallucinations. These improvements resulted in significant benchmark gains, such as an increase in the AA-Omniscience Non-Hallucination Rate from 7.46 to 63.47 and a jump in Tau² Telecom scores from 13.60 to 88.07.",[17,36,38],{"id":37},"deployment-and-tool-use","Deployment and Tool Use",[22,40,41,42,46,47,50,51,54,55,54,58,54,61,64,65,68],{},"Designed for agentic workflows, the model natively outputs Pythonic function calls wrapped in specific special tokens (",[43,44,45],"code",{},"\u003C|tool_call_start|>"," and ",[43,48,49],{},"\u003C|tool_call_end|>","), though this can be overridden to JSON via the system prompt. It offers day-one support across major inference frameworks, including ",[43,52,53],{},"llama.cpp",", ",[43,56,57],{},"MLX",[43,59,60],{},"vLLM",[43,62,63],{},"SGLang",", and ",[43,66,67],{},"ONNX",".",{"title":70,"searchDepth":71,"depth":71,"links":72},"",2,[73,74,75],{"id":19,"depth":71,"text":20},{"id":27,"depth":71,"text":28},{"id":37,"depth":71,"text":38},[77],"AI & LLMs",null,"md",false,{"content_references":82,"triage":92},[83,88],{"type":84,"title":85,"url":86,"context":87},"tool","LFM2.5-8B-A1B","https:\u002F\u002Fhuggingface.co\u002FLiquidAI\u002FLFM2.5-8B-A1B","recommended",{"type":89,"title":90,"url":91,"context":87},"other","Liquid AI Technical Blog","https:\u002F\u002Fwww.liquid.ai\u002Fblog\u002Flfm2-5-8b-a1b",{"relevance":93,"novelty":94,"quality":93,"actionability":94,"composite":95,"reasoning":96},4,3,3.6,"Category: AI & LLMs. The article discusses a new AI model designed for on-device reasoning, which is relevant to AI engineering and addresses the audience's interest in practical applications of AI models. It provides insights into the model's architecture and training improvements, but lacks detailed actionable steps for implementation.",true,"\u002Fsummaries\u002Fa3b9c63845c24fc6-liquid-ai-s-lfm2-5-8b-a1b-efficient-on-device-reas-summary","2026-05-30 14:03:19",{"title":5,"description":70},{"loc":98},"a3b9c63845c24fc6","MarkTechPost","article","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F28\u002Fliquid-ai-releases-lfm2-5-8b-a1b-an-on-device-moe-model-with-8-3b-total-and-1-5b-active-parameters\u002F","summaries\u002Fa3b9c63845c24fc6-liquid-ai-s-lfm2-5-8b-a1b-efficient-on-device-reas-summary",[108,109,110,111],"llm","agents","ai-tools","on-device","Liquid AI's LFM2.5-8B-A1B is a sparse Mixture-of-Experts model that delivers high-performance reasoning and tool-calling on consumer hardware by activating only 1.5B of its 8.3B 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These failures often stem from malformed inputs, unexpected output schemas, or transient environmental errors. Traditional agents often stall or enter infinite loops when a tool call fails, as they lack a systematic mechanism to interpret the error and adjust their strategy. DART (Semantic Recoverability for Structured Tool Agents) addresses this by formalizing a recovery protocol that treats execution errors as semantic signals rather than terminal states.",[17,4317,4319],{"id":4318},"the-dart-recovery-framework","The DART Recovery Framework",[22,4321,4322],{},"Instead of relying on simple retry logic, DART implements a semantic feedback loop that enables the agent to perform three distinct actions upon encountering a tool error:",[4324,4325,4326,4334,4340],"ol",{},[4327,4328,4329,4333],"li",{},[4330,4331,4332],"strong",{},"Error Interpretation:"," The agent analyzes the stack trace or error message to determine if the failure was caused by a syntax error (e.g., invalid JSON), a logical error (e.g., missing required parameters), or an environmental constraint (e.g., rate limiting).",[4327,4335,4336,4339],{},[4330,4337,4338],{},"State Correction:"," Based on the interpretation, the agent modifies its internal state or prompt context to rectify the specific issue. This might involve re-formatting a payload or selecting an alternative tool that achieves the same goal.",[4327,4341,4342,4345],{},[4330,4343,4344],{},"Semantic Re-planning:"," If the initial tool path is fundamentally blocked, the agent uses the error context to re-plan the sequence of operations, ensuring the agent remains focused on the user's high-level objective rather than getting stuck on a single failed step.",[22,4347,4348],{},"By integrating this recovery layer, DART allows agents to maintain continuity in multi-step workflows, effectively turning 'failures' into learning opportunities that refine the agent's future tool-use behavior.",{"title":70,"searchDepth":71,"depth":71,"links":4350},[4351,4352],{"id":4311,"depth":71,"text":4312},{"id":4318,"depth":71,"text":4319},[77],{"content_references":4355,"triage":4362},[4356],{"type":4357,"title":4358,"author":4359,"url":4360,"context":4361},"paper","DART: Semantic Recoverability for Structured Tool Agents","Unknown","https:\u002F\u002Farxiv.org\u002Fabs\u002F2605.23311","cited",{"relevance":4363,"novelty":93,"quality":93,"actionability":93,"composite":4364,"reasoning":4365},5,4.35,"Category: AI & LLMs. The article presents a novel framework (DART) for improving the reliability of AI agents, addressing a specific pain point of execution failures that developers face when integrating AI tools. It offers actionable insights on error interpretation and recovery strategies that can be directly applied in building more resilient AI-powered products.","\u002Fsummaries\u002F33137ecadf93a798-dart-improving-agent-reliability-via-semantic-reco-summary","2026-05-25 07:00:20",{"title":4301,"description":70},{"loc":4366},"33137ecadf93a798","arXiv cs.AI","summaries\u002F33137ecadf93a798-dart-improving-agent-reliability-via-semantic-reco-summary",[109,108,110],"DART (Dynamic Agent Recovery Technique) introduces a framework for structured tool agents to detect and recover from execution failures by leveraging semantic feedback loops, significantly reducing task abandonment.",[],"LYR-QOMAiYZgg-AZg-WiIBZlFpS3smqFpItOdWmmRwo",{"id":4378,"title":4379,"ai":4380,"body":4386,"categories":4432,"created_at":78,"date_modified":78,"description":70,"extension":79,"faq":78,"featured":80,"kicker_label":78,"meta":4433,"navigation":97,"path":4444,"published_at":4445,"question":78,"scraped_at":4446,"seo":4447,"sitemap":4448,"source_id":4449,"source_name":4450,"source_type":104,"source_url":4451,"stem":4452,"tags":4453,"thumbnail_url":78,"tldr":4454,"tweet":78,"unknown_tags":4455,"__hash__":4456},"summaries\u002Fsummaries\u002Fa012b47e318fcbff-claude-dreaming-6x-agent-boost-via-memory-cron-job-summary.md","Claude Dreaming: 6x Agent Boost via Memory Cron Jobs",{"provider":7,"model":4381,"input_tokens":4382,"output_tokens":4383,"processing_time_ms":4384,"cost_usd":4385},"x-ai\u002Fgrok-4.1-fast",3920,1637,27309,0.00158,{"type":14,"value":4387,"toc":4427},[4388,4392,4395,4398,4402,4405,4408,4412],[17,4389,4391],{"id":4390},"memory-optimization-mechanics-deliver-cross-session-intelligence","Memory Optimization Mechanics Deliver Cross-Session Intelligence",[22,4393,4394],{},"Anthropic's Dreaming feature, launched as a research preview at Code w\u002F Claude 2026 on May 6, operates as a scheduled cron job that processes your Claude agent's memory file offline. Between user sessions, it scans all prior interactions, eliminates duplicate entries to reduce bloat, resolves factual contradictions by prioritizing consistent patterns, and extracts multi-session insights—like user preferences or recurring tasks—that no single conversation could detect. This isn't a context window expansion or model upgrade; it's targeted file editing that keeps memory lean and coherent, enabling agents to maintain state across disjointed interactions without hallucinating from noisy data.",[22,4396,4397],{},"To implement a similar system yourself, use the public open-source replica: run a nightly script that ingests session logs, applies deduplication via similarity clustering (e.g., embedding-based cosine thresholds >0.9), merges conflicting facts with confidence scoring, and appends synthesized summaries. This approach scales memory indefinitely without token limits, as the job outputs a compact, structured file Claude reloads on next use.",[17,4399,4401],{"id":4400},"harveys-6x-completion-rate-proves-production-value","Harvey's 6x Completion Rate Proves Production Value",[22,4403,4404],{},"Legal AI startup Harvey reported roughly 6x higher agent task completion rates in internal tests after enabling Dreaming. Agents previously stalled on long-running legal research or multi-step drafting due to memory overload—duplicates caused loops, contradictions led to errors. Post-Dreaming, optimized memory let agents chain 10x more steps reliably, surfacing patterns like \"user prefers concise briefs\" from weeks of chats. This validates Dreaming for production agents: expect 4-8x gains in workflows with >50 sessions, but only if your memory format supports structured edits (JSONL with metadata timestamps works best).",[22,4406,4407],{},"Replicate Harvey's setup by logging sessions to a vector store, then cron the optimization—test on 100-sample legal datasets shows completion jumps from 15% to 85% on chained queries.",[17,4409,4411],{"id":4410},"three-under-discussed-risks-in-auto-dreaming-defaults","Three Under-discussed Risks in Auto-Dreaming Defaults",[22,4413,4414,4415,4418,4419,4422,4423,4426],{},"Despite gains, Dreaming introduces pitfalls: (1) ",[4330,4416,4417],{},"Over-pruning erodes nuance","—aggressive duplicate removal can strip context-specific details, like evolving client instructions; tune similarity thresholds to 0.85 max. (2) ",[4330,4420,4421],{},"Contradiction resolution biases toward recency",", potentially overwriting valid early facts; add user-voted weights or manual review queues. (3) ",[4330,4424,4425],{},"GDPR compliance gaps in defaults","—Auto Dream processes all session data without explicit consent logging, risking fines under EU data minimization rules; implement opt-in flags and anonymization before cron runs. Avoid blindly enabling; audit memory diffs post-job to catch drifts early.",{"title":70,"searchDepth":71,"depth":71,"links":4428},[4429,4430,4431],{"id":4390,"depth":71,"text":4391},{"id":4400,"depth":71,"text":4401},{"id":4410,"depth":71,"text":4411},[77],{"content_references":4434,"triage":4441},[4435,4438],{"type":84,"title":4436,"context":4437},"Harvey","mentioned",{"type":4439,"title":4440,"context":4437},"event","Code w\u002F Claude 2026",{"relevance":4363,"novelty":93,"quality":93,"actionability":4363,"composite":4442,"reasoning":4443},4.55,"Category: AI & LLMs. The article provides a detailed explanation of Anthropic's Dreaming feature, which directly addresses the audience's need for practical AI tooling and optimization strategies. It includes actionable steps for implementing a similar memory optimization system, making it highly relevant and immediately applicable.","\u002Fsummaries\u002Fa012b47e318fcbff-claude-dreaming-6x-agent-boost-via-memory-cron-job-summary","2026-05-15 14:29:17","2026-05-15 15:00:28",{"title":4379,"description":70},{"loc":4444},"a012b47e318fcbff","Level Up Coding","https:\u002F\u002Flevelup.gitconnected.com\u002Fclaude-dreaming-anthropic-memory-explained-a038f17f7d13?source=rss----5517fd7b58a6---4","summaries\u002Fa012b47e318fcbff-claude-dreaming-6x-agent-boost-via-memory-cron-job-summary",[108,109,110],"Anthropic's Dreaming runs a cron job between sessions to prune duplicates, resolve contradictions, and surface patterns in Claude's memory file, delivering 6x higher agent completion rates per Harvey's tests.",[],"A-5IJXRSzmkq11VgJ5B1H0z16Gdg3AXxtIjUwAyGXQc",{"id":4458,"title":4459,"ai":4460,"body":4465,"categories":4743,"created_at":78,"date_modified":78,"description":70,"extension":79,"faq":78,"featured":80,"kicker_label":78,"meta":4744,"navigation":97,"path":4754,"published_at":4755,"question":78,"scraped_at":4756,"seo":4757,"sitemap":4758,"source_id":4759,"source_name":4760,"source_type":4761,"source_url":4762,"stem":4763,"tags":4764,"thumbnail_url":4765,"tldr":4766,"tweet":4767,"unknown_tags":4768,"__hash__":4769},"summaries\u002Fsummaries\u002F172b79615a38a463-build-agent-evals-traces-to-experiments-summary.md","Build Agent Evals: Traces to Experiments",{"provider":7,"model":4381,"input_tokens":4461,"output_tokens":4462,"processing_time_ms":4463,"cost_usd":4464},8665,2929,36004,0.00317485,{"type":14,"value":4466,"toc":4736},[4467,4471,4474,4477,4483,4487,4494,4497,4503,4508,4512,4515,4521,4563,4570,4583,4589,4624,4627,4633,4638,4642,4649,4669,4672,4678,4684,4690,4696,4701,4705,4732],[17,4468,4470],{"id":4469},"replace-vibes-testing-with-systematic-evals-to-catch-regressions","Replace Vibes Testing with Systematic Evals to Catch Regressions",[22,4472,4473],{},"Agents fail silently on untested inputs like adversarial queries, edge cases, or simplistic user phrasing because traditional unit tests break on non-deterministic outputs—same prompt yields varying but potentially correct text. Human review doesn't scale, misses regressions in CI, and can't validate model switches or prompt tweaks without retesting everything. Evals solve this by treating traces (nested JSON logs of LLM\u002Ftool calls with inputs, outputs, metadata like tokens\u002Ftiming) as test data. Run evals in CI to ensure prompt fixes don't hallucinate features or alter tone adversely. Real teams like Decrypt, Bolt, and Anthropic iterated from vibes to evals for production agents.",[22,4475,4476],{},"Agents amplify issues via cascading failures: wrong tool choice, bad parameters, misparsed tool output, or multi-agent routing errors compound into disasters like confusing Tesla (car) with Nikola Tesla in reports. Evals must handle non-prescriptive paths—agents evolve with model upgrades, finding clever shortcuts that break rigid tests. Distinguish capability evals (hard tasks to benchmark improvement) from regression evals (ensure baselines hold). Eval outputs include score, label, and LLM explanations revealing patterns like systematic prompt flaws vs. one-offs.",[22,4478,4479,4482],{},[4330,4480,4481],{},"Quote:"," \"The usual fix unit test doesn't work here... because the same prompt will produce different text on every single run, but those outputs might all be correct.\"",[17,4484,4486],{"id":4485},"trace-first-then-diagnose-failures-before-writing-evals","Trace First, Then Diagnose Failures Before Writing Evals",[22,4488,4489,4490,4493],{},"Start every pipeline with instrumentation: use Phoenix (Arize's open-source observability) to capture spans (LLM\u002Ftool steps) without local install via Phoenix Cloud (free account, API key). Install ",[43,4491,4492],{},"pip install arize-phoenix[crewai] claude-agent-sdk"," (assumes Claude API key; adaptable to OpenAI\u002FGemini). Run pre-built financial analysis agent (Claude-powered, fetches Yahoo Finance data, generates reports) on 13 test queries, auto-tracing to Phoenix UI.",[22,4495,4496],{},"Inspect traces in Phoenix before evals: filter by spans, view inputs\u002Foutputs, identify failure modes manually. Categorize root causes—e.g., model unaware of current year fails forward-looking data; tool param errors; hallucinated facts. Use LLM to auto-categorize eval explanations at scale (LLMs all the way down). This data-driven step skips most tutorials' mistake: writing evals blind, measuring wrong metrics. Example: correctness eval scores 0\u002F13 (can't verify future data), but faithfulness (sticks to sources) scores 13\u002F13—proves eval choice > tuning.",[22,4498,4499,4502],{},[4330,4500,4501],{},"Key principle:"," Read traces to define rubrics—what's \"good\"? For financial agent: accurate tool use, source fidelity, complete reports without extras. Avoid prescriptive evals (e.g., exact tool sequence) that fail smarter agents. Humans build golden datasets for novel failures; code\u002FLLM evals handle volume.",[22,4504,4505,4507],{},[4330,4506,4481],{}," \"We're going to do something that most of the tutorials skip. We're going to actually look at the data. We're going to read our traces, categorize what went wrong, and figure out what to measure before we write a single eval.\"",[17,4509,4511],{"id":4510},"layer-code-built-in-and-custom-llm-evals-for-comprehensive-coverage","Layer Code, Built-in, and Custom LLM Evals for Comprehensive Coverage",[22,4513,4514],{},"Build evals post-tracing, complementary: code for deterministic checks (fast\u002Fcheap), LLM-as-judge for semantics (flexible but costly\u002Fnondeterministic).",[22,4516,4517,4520],{},[4330,4518,4519],{},"Code evals (Python functions):"," Validate JSON output, token limits (\u003C500), required fields, forbidden phrases, keyword presence. Example:",[4522,4523,4527],"pre",{"className":4524,"code":4525,"language":4526,"meta":70,"style":70},"language-python shiki shiki-themes github-light github-dark","def json_eval(output: str) -> dict:\n    try:\n        json.loads(output)\n        return {\"score\": 1.0, \"label\": \"valid\", \"reason\": \"Parses as JSON\"}\n    except:\n        return {\"score\": 0.0, \"label\": \"invalid\", \"reason\": \"JSON parse error\"}\n","python",[43,4528,4529,4537,4542,4547,4552,4557],{"__ignoreMap":70},[4530,4531,4534],"span",{"class":4532,"line":4533},"line",1,[4530,4535,4536],{},"def json_eval(output: str) -> dict:\n",[4530,4538,4539],{"class":4532,"line":71},[4530,4540,4541],{},"    try:\n",[4530,4543,4544],{"class":4532,"line":94},[4530,4545,4546],{},"        json.loads(output)\n",[4530,4548,4549],{"class":4532,"line":93},[4530,4550,4551],{},"        return {\"score\": 1.0, \"label\": \"valid\", \"reason\": \"Parses as JSON\"}\n",[4530,4553,4554],{"class":4532,"line":4363},[4530,4555,4556],{},"    except:\n",[4530,4558,4560],{"class":4532,"line":4559},6,[4530,4561,4562],{},"        return {\"score\": 0.0, \"label\": \"invalid\", \"reason\": \"JSON parse error\"}\n",[22,4564,4565,4566,4569],{},"Run via Phoenix: ",[43,4567,4568],{},"evaluate(pnx.Eval(name=\"json\") .with_code(json_eval), dataset)","—milliseconds, reproducible.",[22,4571,4572,4575,4576,54,4579,4582],{},[4330,4573,4574],{},"Built-in LLM evals (Arize Phoenix):"," ",[43,4577,4578],{},"pnx.qa_correctness",[43,4580,4581],{},"pnx.answer_relevancy","—prompt powerful LLM (e.g., Claude-3.5-Sonnet) vs. agent output\u002Freference. Scores 0-1 with explanations.",[22,4584,4585,4588],{},[4330,4586,4587],{},"Custom LLM rubric evals:"," Define rules in prompt, add few-shot examples from traces. For faithfulness:",[4522,4590,4592],{"className":4524,"code":4591,"language":4526,"meta":70,"style":70},"faithfulness_eval = pnx.LLMEval(\n    name=\"faithfulness\",\n    prompt_template=\"\"\"Judge if {output} is faithful to {sources}...\"\"\",\n    examples=[{\"input\": ..., \"output\": ..., \"reference\": ..., \"score\": 1.0, \"explanation\": ...}],\n    model=\"claude-3-5-sonnet-20240620\"\n)\n",[43,4593,4594,4599,4604,4609,4614,4619],{"__ignoreMap":70},[4530,4595,4596],{"class":4532,"line":4533},[4530,4597,4598],{},"faithfulness_eval = pnx.LLMEval(\n",[4530,4600,4601],{"class":4532,"line":71},[4530,4602,4603],{},"    name=\"faithfulness\",\n",[4530,4605,4606],{"class":4532,"line":94},[4530,4607,4608],{},"    prompt_template=\"\"\"Judge if {output} is faithful to {sources}...\"\"\",\n",[4530,4610,4611],{"class":4532,"line":93},[4530,4612,4613],{},"    examples=[{\"input\": ..., \"output\": ..., \"reference\": ..., \"score\": 1.0, \"explanation\": ...}],\n",[4530,4615,4616],{"class":4532,"line":4363},[4530,4617,4618],{},"    model=\"claude-3-5-sonnet-20240620\"\n",[4530,4620,4621],{"class":4532,"line":4559},[4530,4622,4623],{},")\n",[22,4625,4626],{},"Meta-evaluate judges: run golden dataset through your eval, score agreement (e.g., 90%+ reliable). Use stronger model for judging.",[22,4628,4629,4632],{},[4330,4630,4631],{},"When to use:"," Code for format\u002Flength; LLM for accuracy, faithfulness, tone. Agents need end-to-end: tool selection, params, output parsing.",[22,4634,4635,4637],{},[4330,4636,4481],{}," \"Choosing the right eval matters more than tuning it. A correctness eval scored 0 out of 13 on the same agent that a faithfulness eval scored 13 out of 13.\"",[17,4639,4641],{"id":4640},"datasets-and-experiments-prove-iterations-work","Datasets and Experiments: Prove Iterations Work",[22,4643,4644,4645,4648],{},"Create datasets from traces: ",[43,4646,4647],{},"dataset = pnx.Dataset.from_pandas(traces_df)"," or golden sets (human-labeled). Run experiments: baseline vs. prompt variants.",[4522,4650,4652],{"className":4524,"code":4651,"language":4526,"meta":70,"style":70},"exp = pnx.Experiment(name=\"prompt-v2\", trace_dataset=dataset)\nexp.log_evals([json_eval, faithfulness_eval], variant=\"v2_prompt\")\nexp.compare()  # Tables\u002Fcharts: scores, spans, explanations\n",[43,4653,4654,4659,4664],{"__ignoreMap":70},[4530,4655,4656],{"class":4532,"line":4533},[4530,4657,4658],{},"exp = pnx.Experiment(name=\"prompt-v2\", trace_dataset=dataset)\n",[4530,4660,4661],{"class":4532,"line":71},[4530,4662,4663],{},"exp.log_evals([json_eval, faithfulness_eval], variant=\"v2_prompt\")\n",[4530,4665,4666],{"class":4532,"line":94},[4530,4667,4668],{},"exp.compare()  # Tables\u002Fcharts: scores, spans, explanations\n",[22,4670,4671],{},"Visualize regressions, filter low-scorers, iterate prompts. Scale to thousands: patterns emerge (e.g., budget query misses costs).",[22,4673,4674,4677],{},[4330,4675,4676],{},"Advanced frameworks:"," Impact hierarchy (prioritize high-failure evals); data flywheel (evals → insights → prompts → better traces); pairwise (A\u002FB outputs); reliability scoring (judge variance).",[22,4679,4680,4683],{},[4330,4681,4682],{},"Common pitfalls:"," Overly brittle code evals; unaligned LLM judges (meta-eval fixes); ignoring cascades\u002Fmulti-agents; prescriptive tests.",[22,4685,4686,4689],{},[4330,4687,4688],{},"Quality criteria:"," 100% regression suite; explanations actionable; CI-runnable; humans validate outliers.",[22,4691,4692,4695],{},[4330,4693,4694],{},"Practice:"," Fork speaker's notebook (GitHub\u002Fseldo), trace your agent, build 3 evals, experiment on 50+ traces. Prerequisites: Python, LLM API, basic agents (2+ years dev exp).",[22,4697,4698,4700],{},[4330,4699,4481],{}," \"Without evals you can't change your system prompt to fix a tone issue because the tone might get better but suddenly the bot might be hallucinating product features.\"",[17,4702,4704],{"id":4703},"key-takeaways","Key Takeaways",[4706,4707,4708,4711,4714,4717,4720,4723,4726,4729],"ul",{},[4327,4709,4710],{},"Instrument with Phoenix traces before any evals—inspect spans to pinpoint failures like wrong tools or date ignorance.",[4327,4712,4713],{},"Layer evals: code for deterministic (JSON, length), LLM for semantic (faithfulness > correctness for sourced tasks).",[4327,4715,4716],{},"Meta-evaluate LLM judges on golden data to ensure reliability >90%.",[4327,4718,4719],{},"Use capability evals for new skills, convert to regressions; run experiments to validate changes, not eyeballing.",[4327,4721,4722],{},"Categorize failures from explanations to fix systematic prompt issues at scale.",[4327,4724,4725],{},"Avoid prescriptive tests—agents optimize paths; focus non-brittle metrics.",[4327,4727,4728],{},"Humans for golden sets\u002Foutliers only; evals scale CI for model\u002Fprompt upgrades.",[4327,4730,4731],{},"Start simple: 13-query financial agent → full pipeline in notebook.",[4733,4734,4735],"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":70,"searchDepth":71,"depth":71,"links":4737},[4738,4739,4740,4741,4742],{"id":4469,"depth":71,"text":4470},{"id":4485,"depth":71,"text":4486},{"id":4510,"depth":71,"text":4511},{"id":4640,"depth":71,"text":4641},{"id":4703,"depth":71,"text":4704},[77],{"content_references":4745,"triage":4752},[4746,4749],{"type":84,"title":4747,"url":4748,"context":87},"Arize Phoenix","https:\u002F\u002Fphoenix.arize.com\u002F",{"type":84,"title":4750,"author":4751,"context":87},"Claude","Anthropic",{"relevance":4363,"novelty":93,"quality":93,"actionability":93,"composite":4364,"reasoning":4753},"Category: AI & LLMs. The article provides a detailed framework for implementing systematic evaluations of AI agents, addressing a key pain point for developers who struggle with traditional testing methods. It offers actionable steps, such as using Phoenix for instrumentation, which can be directly applied to improve testing processes.","\u002Fsummaries\u002F172b79615a38a463-build-agent-evals-traces-to-experiments-summary","2026-05-14 18:00:06","2026-05-14 23:00:14",{"title":4459,"description":70},{"loc":4754},"172b79615a38a463","AI Engineer","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Xfl50508LZM","summaries\u002F172b79615a38a463-build-agent-evals-traces-to-experiments-summary",[109,108,110],"https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FXfl50508LZM\u002Fhqdefault.jpg","Replace vibes-based testing with a full eval pipeline: trace agent runs with Phoenix, categorize failures from data, build code\u002FLLM evals, run experiments to validate prompt changes on a financial agent.","Hands-on workshop by [Laurie Voss](https:\u002F\u002Fx.com\u002Fseldo) where you code along building an eval pipeline for a financial analysis agent: Phoenix tracing, failure categorization, code evals, LLM-as-judge (Arize-built and custom), and experiments to test prompt changes.",[],"nQ3a3NVfZARLm-VZlTYTQNTtmNfMM2erqwuLoai8SRk",{"id":4771,"title":4772,"ai":4773,"body":4778,"categories":4829,"created_at":78,"date_modified":78,"description":70,"extension":79,"faq":78,"featured":80,"kicker_label":78,"meta":4830,"navigation":97,"path":4852,"published_at":4853,"question":78,"scraped_at":4854,"seo":4855,"sitemap":4856,"source_id":4857,"source_name":4858,"source_type":104,"source_url":4859,"stem":4860,"tags":4861,"thumbnail_url":78,"tldr":4862,"tweet":78,"unknown_tags":4863,"__hash__":4864},"summaries\u002Fsummaries\u002F1e8b4fa0c073eae3-ai-glossary-master-terms-for-building-with-llms-summary.md","AI Glossary: Master Terms for Building with LLMs",{"provider":7,"model":4381,"input_tokens":4774,"output_tokens":4775,"processing_time_ms":4776,"cost_usd":4777},9350,2293,20789,0.002994,{"type":14,"value":4779,"toc":4823},[4780,4784,4787,4790,4794,4797,4800,4803,4807,4810,4813,4817,4820],[17,4781,4783],{"id":4782},"core-ai-architectures-powering-modern-tools","Core AI Architectures Powering Modern Tools",[22,4785,4786],{},"Large language models (LLMs) underpin assistants like ChatGPT, Claude, Gemini, Llama, Copilot, and Le Chat. These deep neural networks, with billions of parameters (weights), map word relationships from vast datasets of books, articles, and transcripts. When prompted, they predict the most likely next tokens. Neural networks form their backbone: multi-layered structures mimicking brain neurons, enabling deep learning to auto-discover data features without manual engineering. Deep learning needs millions+ data points and extended training, driving high costs but yielding complex correlations beyond simple ML like decision trees.",[22,4788,4789],{},"AGI remains vague: Sam Altman calls it a 'median human co-worker'; OpenAI's charter defines it as autonomous systems outperforming humans in most economically valuable work; DeepMind sees it as matching humans on cognitive tasks. Even experts disagree, so prioritize narrow capabilities over chasing AGI hype when building.",[17,4791,4793],{"id":4792},"training-optimization-and-deployment-trade-offs","Training, Optimization, and Deployment Trade-offs",[22,4795,4796],{},"Distillation transfers knowledge from a large 'teacher' model to a smaller 'student' by recording outputs and retraining—creating efficient versions like GPT-4 Turbo. It risks ToS violations if distilling competitors' APIs. Fine-tuning adapts pre-trained LLMs with domain-specific data for targeted tasks, letting startups specialize general models.",[22,4798,4799],{},"Inference runs trained models to generate predictions; it demands optimized hardware (GPUs, TPUs) as large models crawl on laptops. Memory cache like KV caching speeds this in transformers by reusing computations, slashing power and latency for repeated queries. Compute denotes the GPUs\u002FCPUs fueling training\u002Finference—the AI economy's bottleneck.",[22,4801,4802],{},"Hallucinations occur when LLMs fabricate facts from training gaps, risking misinformation (e.g., bad medical advice). Mitigate with domain-specific fine-tuning to close knowledge holes.",[17,4804,4806],{"id":4805},"generation-techniques-and-reasoning-boosts","Generation Techniques and Reasoning Boosts",[22,4808,4809],{},"Diffusion models generate art\u002Fmusic\u002Ftext by learning to reverse 'noise destruction' of data, enabling realistic outputs from randomness. GANs pit generator vs. discriminator networks to refine fakes like deepfakes, best for narrow tasks like images\u002Fvideos.",[22,4811,4812],{},"Chain-of-thought prompting breaks problems into steps (e.g., legs\u002Fheads riddle: 20 chickens, 20 cows), improving LLM accuracy on logic\u002Fcoding via reasoning models optimized with reinforcement learning. This trades speed for reliability.",[17,4814,4816],{"id":4815},"agents-unlock-autonomous-workflows","Agents Unlock Autonomous Workflows",[22,4818,4819],{},"AI agents chain LLMs with tools for multi-step tasks like booking or expense filing, using API endpoints as 'buttons' to control services autonomously. Coding agents extend this to dev workflows: writing, testing, debugging, and fixing code across repos—like tireless interns needing review.",[22,4821,4822],{},"Infrastructure lags, but agents amplify automation; pair with RAG (not detailed here) to ground outputs and curb hallucinations.",{"title":70,"searchDepth":71,"depth":71,"links":4824},[4825,4826,4827,4828],{"id":4782,"depth":71,"text":4783},{"id":4792,"depth":71,"text":4793},{"id":4805,"depth":71,"text":4806},{"id":4815,"depth":71,"text":4816},[77],{"content_references":4831,"triage":4849},[4832,4835,4838,4843,4846],{"type":89,"title":4833,"url":4834,"context":4361},"OpenAI Charter","https:\u002F\u002Fopenai.com\u002Fcharter\u002F",{"type":89,"title":4836,"url":4837,"context":4361},"Sam Altman Artificial Intelligence OpenAI Profile","https:\u002F\u002Fnymag.com\u002Fintelligencer\u002Farticle\u002Fsam-altman-artificial-intelligence-openai-profile.html",{"type":4839,"title":4840,"publisher":4841,"url":4842,"context":4361},"report","A Primer on Compute","Carnegie Endowment","https:\u002F\u002Fcarnegieendowment.org\u002Fposts\u002F2024\u002F04\u002Fa-primer-on-compute",{"type":89,"title":4844,"url":4845,"context":4361},"A Brief History of Diffusion, the Tech at the Heart of Modern Image-Generating AI","https:\u002F\u002Ftechcrunch.com\u002F2022\u002F12\u002F22\u002Fa-brief-history-of-diffusion-the-tech-at-the-heart-of-modern-image-generating-ai\u002F",{"type":89,"title":4847,"url":4848,"context":4361},"KV Caching","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fnot-lain\u002Fkv-caching",{"relevance":4363,"novelty":94,"quality":93,"actionability":93,"composite":4850,"reasoning":4851},4.15,"Category: AI & LLMs. The article provides a glossary of key AI terms that are essential for integrating LLMs effectively, addressing the audience's need for practical applications in AI product development. It includes actionable insights on techniques like distillation and fine-tuning, which are directly applicable to building AI-powered products.","\u002Fsummaries\u002F1e8b4fa0c073eae3-ai-glossary-master-terms-for-building-with-llms-summary","2026-05-09 21:45:00","2026-05-10 15:26:48",{"title":4772,"description":70},{"loc":4852},"1e8b4fa0c073eae3","TechCrunch AI","https:\u002F\u002Ftechcrunch.com\u002F2026\u002F05\u002F09\u002Fartificial-intelligence-definition-glossary-hallucinations-guide-to-common-ai-terms\u002F","summaries\u002F1e8b4fa0c073eae3-ai-glossary-master-terms-for-building-with-llms-summary",[108,109,110],"Decode 20+ key AI terms like AGI, chain-of-thought, distillation, and agents to integrate LLMs effectively, avoid pitfalls like hallucinations, and optimize for production.",[],"-mfvo92I8dJbP3YpZi0ZbXkyGrxjv2tAKNEhGABP5f4"]