#prompt-engineering
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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.
Master Cursor /goal: Fix Premature Stops on Complex Tasks
Cursor's /goal uses LLM judgment to loop agents on long tasks like 9-hour migrations, preventing lazy early exits—define explicit 'done' criteria with verifiable tests (e.g., Playwright) and quantify metrics to succeed.
Claude + Higgsfield MCP Builds 3 Agency Ad Tools in One Session
Integrate Higgsfield MCP into Claude Code to generate Shopify creative packs, counter 1-star Amazon reviews with UGC ads, and create consistent AI influencers—all from single prompts, replacing full agency workflows.
Agentic Search Powers 80% of LLM Context Engineering
Context engineering relies on agentic search tools to pull relevant data from files, DBs, web, and memory. Master tool descriptions, skills, and shell tools to avoid brittle retrieval—demoed with ElasticSearch and LangChain.
AI EngineerPre-Mortem Prompts Fix Claude's Yes-Man Bias
Claude flatters plans due to RLHF; prompt it to assume failure in 6 months and explain why to get honest risk analysis—Kahneman's top decision tool, invented by Klein in 1989.
Optimize Live Agents: GEPA Prompts + Managed Vars
Tune production agents without redeploys using Logfire's managed variables for prompts/models and GEPA's genetic algorithm to evolve better prompts from evals on golden datasets.
AI EngineerAgent Observability: Signals and Self-Diagnostics
Shift from evals to production monitoring using explicit signals (errors, latency), implicit signals (frustration, refusals via classifiers/regex), experiments, and agent self-diagnostics to catch issues early in complex, non-deterministic agents.
LLM Outputs Vary Across Runs: 6 Models Tested 3x Each
Opus and GPT-4o nailed Filament enum task 3/3 times; Gemini 2/3; GLM 1/3; others failed. Even top models differ in UI details like textarea rows=8 or sortable badges across runs—always review code.
Python Rules Turn Financial Signals into Thesis Verdicts
Classify stock theses into 10 claim types, map price/fundamentals signals to support/against/missing evidence using thresholds like drawdown >-15% or P/E<20, then assign verdicts like 'supported' based on evidence counts and gaps for a research copilot.
Guarantee LLM Outputs Match Exact Taxonomies with Tries
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.
Design.md: AI's Blueprint for Consistent Custom Design
Google's Design.md files capture typography, colors, and effects as portable 'design DNA'—attach to prompts to eliminate drift and create unique outputs across web, slides, motion, and apps using AI agents.
Greg IsenbergBuild AI Skills for Repeatable Agent Tasks
Skills are portable markdown folders with frontmatter, constraints, and scripts that teach LLMs specific, reliable workflows—codifying DRY principles for agents across repos and teams.
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.
Bulletproof 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.
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