#prompt-engineering
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Escaping LLM Homogeneity with Meta-Persona Anchoring
To combat output uniformity in LLMs, use Meta-Persona Anchoring to define high-level cognitive constraints and Sequential Temperature Scaling to manage creative variance across multi-step reasoning chains.
Energy-Efficient Prompting: The Impact of Keywords on On-Device LLMs
On-device LLM energy consumption is highly sensitive to specific prompt keywords, meaning developers can optimize battery life and performance by selecting energy-efficient tokens.
Evals-Driven Development for High-Stakes Mental Health AI
SonderMind builds safe mental health AI by replacing generic model guardrails with a modular, clinician-led evaluation loop that treats clinical judgment as code.
AI EngineerBuilding Production-Grade Agent Evals: A Practical Framework
Reliable AI agents require a loop of iterative evaluation that prioritizes patterns over individual failures, starting with intuition-based 'vibing' before scaling to rigorous, rubric-driven golden sets.
AI EngineerAI Builder Essentials: Tokens, RAG, and Context Windows
LLMs operate on tokens—not words—and are inherently non-deterministic. To overcome training data cutoffs, use Retrieval-Augmented Generation (RAG) to inject real-time data, while managing context window limits and token costs to avoid inefficient 'token maxxing'.
Google Cloud TechSecuring Multi-Agent Systems with Model Armor
Protect multi-agent systems from indirect prompt injection, PII leaks, and malicious content by implementing Model Armor as a centralized security guardrail at every system boundary.
Google Cloud TechWhen to Fine-Tune vs. Use RAG and Prompt Engineering
Fine-tuning is no longer the default for customization; modern frontier models often outperform custom-trained ones. Prioritize RAG, context engineering, and agent skills before considering fine-tuning for specific bottlenecks.
The Steering Budget: Why Examples Outperform Prompt Knobs
When steering LLMs, providing concrete examples is significantly more effective than adjusting abstract system prompt 'knobs' or parameters, as examples provide clearer context for model behavior.
Getting Started with ChatGPT: A Practical Guide
ChatGPT is a conversational AI assistant designed to help with writing, brainstorming, and problem-solving. Success starts with simple chat-based tasks and evolves into structured workflows as you identify repeatable processes.
Scaling Model Robustness via Automated Red-Teaming
OpenAI developed GPT-Red, an automated red-teaming model trained via self-play, to identify vulnerabilities and adversarially train future models, resulting in significant improvements in prompt injection resistance.
Making LLM Self-Evolution Safe with Held-Out Selection
RSEA improves LLM agent performance by recursively evolving natural-language artifacts while using a strict held-out validation gate to prevent performance regression.
Building Great Agent Skills: The Missing Manual
To escape 'skill hell,' developers must treat agent skills as structured, maintainable code by optimizing triggers, minimizing context bloat, using 'leading words' for steering, and aggressively pruning irrelevant instructions.
AI EngineerImproving LLM Planning with Symbolic Feedback Loops
To solve LLM planning errors in long-horizon tasks, this framework uses symbolic verification to provide corrective, interpretable feedback, forcing the model to iteratively refine its plans.
Personality Prompting in Multi-Agent Teams: Impact vs. Task Structure
Personality manipulation in LLM agents significantly alters communication style but only degrades performance in open-ended or competitive tasks, while having negligible impact on structured coding tasks.
The Promptware Kill Chain: Securing AI Agents
Promptware is a new class of malware that exploits the lack of separation between instructions and data in LLMs. To defend against it, builders must adopt a zero-trust architecture, treating AI agents as untrusted, hostile runtimes rather than benign assistants.
IBM TechnologyControlling LLM Output: Deterministic vs. Stochastic Generation
LLM outputs are probability distributions over tokens. You can force deterministic results by setting temperature to 0 or using top-p/top-k sampling to constrain the randomness of the next-token selection.
The Mechanics and Risks of AI Prompt Injection
AI agents cannot distinguish between developer instructions and untrusted data, making them vulnerable to prompt injection attacks where hidden text in web pages overrides system commands.
Stop Writing Tone Instructions: Use a 4-Layer AI Architecture
Stop relying on a single system prompt for brand voice. Instead, use a four-layer architecture—Immutable Identity, Situational Mode, Example-Anchored Voice, and a Deterministic Veto—to separate instructions from verification.
Improving LLM Ethical Reasoning with Narration-of-Thought
Narration-of-Thought (NoT) is an inference-time prompting scaffold that forces LLMs to explicitly identify stakeholders and uncertainties before committing to a decision, significantly reducing common ethical reasoning failures.
Instruction Bleed: The Hidden Risk of Prompt Composition
Compositional Behavioral Leakage (CBL) occurs when prompt modules interfere with each other within a shared context window, causing silent, sub-threshold shifts in agent behavior that standard QA often misses.
Building AI-Powered Apps: A Low-Code Guide for Small Teams
Small teams can modernize legacy applications by leveraging 'vibe coding' and managed database AI features like hybrid search and vector embeddings, allowing them to implement semantic capabilities without needing a team of AI experts.
Google Cloud TechThe Miranda Hypothesis: Why Persona Evals Fail
Current persona-based AI benchmarks measure 'convincingness' rather than historical fidelity, leading to 'Miranda distortion' where models prioritize culturally dominant narratives (like the Hamilton musical) over primary documentary records.
Verifying LLM Reasoning Traces with VeryTrace
VeryTrace improves LLM reliability by formalizing natural language reasoning into a structured, compilable DSL, enabling automated verification and error repair without domain-specific training.
AI Agents vs. Social Engineering: The Future of Trust
AI-native operating systems may finally solve social engineering by removing humans from routine trust decisions, though this shifts the battlefield to AI-agent manipulation and prompt injection.
Building Functional Personas with AI for User-Centric Decisions
Move beyond static, demographic-heavy personas by using AI to synthesize research into 'functional' personas focused on user goals, tasks, and objections, then making them interactive via custom chatbots.
Smashing MagazineOptimizing LLM Skills with Microsoft SkillOpt
Microsoft SkillOpt provides an automated pipeline to iteratively improve LLM prompt-based skills through a cycle of rollout, reflection, and validation, allowing developers to quantitatively measure performance gains against a baseline.
How AI Memory Tools Introduce Bias and Degrade Accuracy
Research shows that AI memory systems often fail to distinguish between relevant context and irrelevant user preferences, causing models to become sycophantic and prioritize user-fed misconceptions over objective accuracy.
Optimizing Long-Horizon AI Agents via Context Engineering
The paper demonstrates that reducing context noise in long-horizon LLM agents significantly improves performance and reliability, challenging the 'more context is better' paradigm.
Anthropic's Mythos-Class Models: Fable 5 and Mythos 5 Explained
Anthropic has introduced the 'Mythos-class' model tier, featuring Claude Fable 5 (general release with safety classifiers) and Claude Mythos 5 (limited, unrestricted release). Both models offer 1M token context windows and advanced reasoning capabilities.
Diagnosing Instruction Hierarchy Failures in Reasoning LLMs
Reasoning models often fail when instructions conflict or are poorly prioritized; this research identifies the structural causes of these hierarchy breakdowns and proposes methods to repair them.
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