#prompt-engineering
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Customize VS Code Copilot Agents for Repeatable Workflows
Use VS Code's Customization UI to build custom instructions, agent skills, agents, hooks, and prompt files—define behaviors once for consistent AI outputs across chats, teams, and projects without extensions.
Visual Studio CodeBulletproof Taste: Rejections Beat AI Gingerbread
AI erodes taste by mimicking style without judgment—counter it by collecting rejections as breadcrumbs, diagnosing drift with prompts, and feeding taste high-conviction work that demands discomfort.
AI Studio's Visual Upgrades Make Vibe Coding Iterative
Tab Tab Tab autocompletes prompts, design previews steer themes early, and edit mode enables direct UI tweaks—turning AI Studio into a visual app builder for fast prototypes.
AI Workflow: Context, Config, Verify, Delegate, Loop
Treat AI as a collaborator: Organize context in ~/src and ~/vault with INDEX.md and CLAUDE.md for onboarding; encode preferences hierarchically in CLAUDE.md files and on-demand skills; verify via hooks like ruff and self-checks; delegate big tasks across 3-6 parallel sessions; mine transcripts of ~2,500 turns to update configs for compounding gains.
Context Engineering Beats Prompt Engineering for Reliable LLMs
Prompt engineering falls short for production LLM apps; context engineering delivers by systematically providing instructions, memory, RAG, tools, and filtering—turning vague queries into precise actions.
3 Steps to Custom Claude Code Agentic OS
Codify workflows into domains, tasks, skills, and automations; add Obsidian memory layer; build observability dashboard to track, optimize, and share with teams/clients ahead of 99% of users.
China's Info Seeking: Mobile GenAI + Social, Mirrors West
Chinese users abandon ad-clogged Baidu for mobile genAI (DeepSeek, Doubao) and social apps (Douyin, Rednote) but exhibit identical prompting, trust, and AI-literacy patterns as North Americans.
Fix Prompt Fragility by Decomposing Agents into Microservices
Monolithic LLM prompts fail unpredictably from tiny changes because one model juggles routing, reasoning, validation, and more—decompose into sub-agents and nano models to shrink context 50-80%, cut costs 60-80%, and eliminate cascades.
Harness Beats Model: 6x Agent Performance Gap
Stanford/Tsinghua papers prove agent orchestration (harness) causes 6x performance variation on the same model; optimize harness via subtraction and natural language before switching models.
Verifier Agent Crushes AI Coding Review Bottleneck
Stack a verifier agent (GPT-5.5) on your builder (Opus 4.7) to auto-validate outputs via atomic claims, reprompt on failures, and template engineering rules—spending tokens to save review time.
AI Video Pipeline: Claude + Higgsfield Masterclass
Connect Claude to Higgsfield's MCP to generate consistent character videos, UGC ads, and cinematic stories via reference sheets, structured prompts, and storyboards—bypassing high costs, skills gaps, and slow production.
5 LLM Agent Patterns for Reliable, Bloat-Free Workflows
Use prompt chaining, routing, parallelization, orchestrator-workers, and evaluator-optimizer patterns to build production-ready LLM agents; start with simple workflows unless tasks demand adaptive reasoning, prioritizing tool interfaces, docs, and logging.
5 Prompt Techniques for Reliable LLM Outputs
Role-specific personas, negative constraints, JSON schemas, ARQ checklists, and verbalized sampling make LLM prompts produce consistent, structured results without fine-tuning or model changes.
Engineer AI Context Like Code: Full Lifecycle
Treat AI agent context as code with a Context Development Lifecycle—Generate, Evaluate, Distribute, Observe—to create reliable, scalable prompts that drive better agent outputs via testing, sharing, and feedback loops.
Fix AI Note Forgetting: Unlock LLM Mechanics via RAG
Structure notes in consistent Markdown, retrieve relevant chunks to fit context windows (measured in tokens), instruct model to use only provided notes to avoid hallucinations, and tune temperature for consistent explanations or varied practice questions.
Fix Tokenization Drift by Matching SFT Token Patterns
Minor formatting like spaces or newlines causes tokenization drift, shifting prompts out-of-distribution and dropping accuracy. Use Jaccard token overlap (>80% safe) to measure risk; Automated Prompt Optimization (APO) selects best templates, boosting simulated accuracy from 40-50% to 83%.
Frontier LLMs Split: Claude Deontological, Grok Consequentialist
Philosophy Bench benchmark of 100 ethical dilemmas reveals Claude complies with only 24% of norm-violating requests, Grok executes most freely, Gemini steers easiest via prompts, and GPT avoids moral reasoning with 12.8% error rate.
Build Observable Gmail Agents in n8n with Human Controls
Create secure AI workflows in n8n that manage Gmail/Calendar via chat, with built-in observability, granular tool permissions, and human approvals to avoid black-box agents.
AI Engineer4 D's Replace Mega-Prompts for GPT-5.5
State-of-the-art models like GPT-5.5, Opus 4.7, and Gemini 3.1 Pro outperform step-by-step prompts; specify Destination, Definition, Doubt, and Done to leverage their pathfinding intelligence without bottlenecking.
Claude Code Mastery: 6 Levels to Autonomous Agents
Master Claude Code through 6 progressive levels: from basic installs and prompting to custom skills, sub-agents, parallel teams, and cloud-based autonomous agents running routines while you sleep.