#prompt-engineering
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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.
Automating Prompt Optimization with GEPA Reflective Evolution
GEPA automates prompt engineering by using a reflection model to iteratively refine prompts based on structured feedback from a deterministic evaluation pipeline.
AI Comprehension Over Generation: The 'Catch Me Up' Workflow
In complex, legacy codebases, the primary value of AI is not code generation but comprehension. By using structured prompts to build mental models before planning or implementation, developers can avoid 'slop' and maintain high code quality.
AI EngineerQwen3.7-Max: Reasoning-First Agent Model with 1M Context
Alibaba's Qwen3.7-Max is a text-only reasoning model featuring a 1M-token context window and an 'extended-thinking' mode designed for complex, multi-step agentic workflows and code refactoring.
Long Context vs. Cache Augmented Generation (CAG)
Long context is best for one-off document analysis, while Cache Augmented Generation (CAG) and prompt caching optimize performance and cost for repeated queries against stable knowledge bases by reusing pre-computed KV caches.
Scaling Coding Agents: Lessons from Building Langfuse Skills
To make coding agents reliable, move away from static pre-training context toward dynamic, search-based documentation retrieval and rigorous evaluation, while carefully defining target functions to avoid optimizing away reliability.
AI EngineerOptimizing System Prompts via Embedding by Elicitation
The paper introduces 'Embedding by Elicitation,' a method that uses Bayesian Optimization to dynamically refine system prompts by learning latent representations, overcoming the limitations of static prompt engineering.
Building Long-Running AI Agents: Harnesses and Adversarial Loops
To build agents that run for hours without losing coherence, move beyond single-session loops. Use adversarial 'generator-critic' architectures, structured handoffs, and persistent state files to maintain focus and quality over long horizons.
AI EngineerWider Harness: 6D Framework for Digital Workers
Evolve task agents into digital workers handling recurring functions using a 6D harness: Identity, Context, Capability, Conduct, Cognition, Governance—onboard like hires, not deploy like tasks.
Poetiq Meta-System Auto-Builds Harnesses Boosting All LLMs on LCB Pro
Poetiq’s Meta-System uses recursive self-improvement to automatically generate model-agnostic inference harnesses, lifting every tested LLM's LiveCodeBench Pro score without fine-tuning—e.g., Gemini 3.1 Pro from 78.6% to 90.9%, GPT 5.5 High to 93.9%.
Chess Coach Pipeline: Engines + Detectors + LLM Translator
LLMs fail at chess due to hallucinations; fix by using Stockfish for evaluation, tactical/positional detectors for concepts, and LLM only to translate into natural language—achieving sub-3s latency without errors.
AI EngineerOptimize Claude Limits: Plan, Remember, Pick Models Wisely
Claude limits stem from unnecessary token waste via vague prompts, retained context, repeats, wrong models/tools—fix by planning convos, adding memory, batching, and model selection to treat it as a work system.
Codex Prompts Automate Finance Reporting and Models
Finance teams cut assembly time on MBR narratives, model cleanups, CFO packs, variance bridges, and forecasts by feeding Codex existing spreadsheets, dashboards, and notes via copy-paste prompts that cite sources and flag risks—no coding required.
Malleable Evals: Adaptive Testing for Changing AI Agents
Static benchmarks fail self-adapting agents; use production traces for agent-curated, always-on eval suites that self-optimize toward user intent.
AI EngineerGM Cuts 600 IT Jobs to Hire AI-Native Engineers
GM laid off 600 IT workers (10% of department) to recruit specialists in agent/model development, prompt engineering, data pipelines—showing enterprises must rebuild teams for production AI, not just add tools.
Stitch: Google's Free AI for Stunning UIs, No Design Needed
Google Labs' Stitch generates responsive, production-ready UIs from natural language prompts, exports HTML/Tailwind CSS, and integrates with agents like Gemini CLI—perfect for backend devs prototyping fast.
Harness Engineering: Stack Rules, Skills & Agents for Reliable AI Dev
Harness Engineering builds reliable AI code generation by stacking Rules (guidelines), Skills (SOPs), Sub-Agents (roles), Workflows (handoffs), Scripts (gates), and MCP (external tools) into a verifiable system, demonstrated in a minimal Go CLI project.
HTML Replaces Markdown for Interactive AI Outputs
Prompt AI agents for single-file HTML instead of long Markdown reports to create navigable, editable, interactive artifacts that humans can actually use, review, share, and act on.
Mobbin MCP Links 600k UI Screens to Claude/Codex for Pro Designs
Connect Mobbin's 600k app screens to Claude Code or Codex via MCP to generate realistic banking dashboards, competitive reports from 25+ apps, and client-ready mood boards in 5-10 minutes instead of 4 hours.
Agentic Consent: Dynamic Permissions for Safe AI Agents
Agentic consent uses identity governance, granular time-bound permissions, and just-in-time prompts to ensure AI agents act responsibly in changing environments, acting with humans rather than instead of them.
IBM TechnologyHTML Beats Markdown for AI Specs at 2-4x Token Cost
Switch specs, plans, PRs from Markdown to HTML for tables, SVG diagrams, JS interactions—8x richer density. Claude Opus 4.7's 1M context absorbs 2-4x tokens; outputs boost readability so humans stay in the loop.
DIY Smart Code4-Step Audit Catches AI's 'Almost Right' Errors
For high-stakes AI outputs (financial/legal), finish your artifact, then in fresh chats: split into factual claims, validate against source with 4 labels (supported/conflicts/no proof/needs human), and rewrite fixes subtle lies that sound plausible.
HTML Beats Markdown for LLM Outputs
Request HTML from LLMs like Claude instead of Markdown to generate interactive SVGs, widgets, and navigable explanations—token limits no longer justify Markdown's efficiency.
AI Agents Need Scaffolding: Prompts to Plugins Guide
Most waste 40% of AI time on prompts for repeatable tasks. Build agent 'mech suits' with skills for house style, plugins for full workflows, MCPs for data access, and hooks/scripts for reliability—reusable across teams and LLMs.
7 Skills to Engineer Production AI Agents
Shift from prompt engineering to agent engineering: master system design, tool contracts, RAG, reliability, security, observability, and product thinking to build agents that act reliably in the real world.
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