#prompting
17 articles
Prompt Versioning, Evaluation, and LLMOps: Treating Prompts Like Code
How production teams version, test, and regression-test prompts the same way they ship software — CI/CD gates, eval datasets, and the tools that make it tractable.
Prompt Anti-Patterns: The Failure Modes That Ship to Production Silently
Ten documented prompt defects — instruction-stacking, few-shot pollution, persona-stuffing — and why they survive code review and only surface under production load.
Context window management for agents: select, compress, isolate
How to keep agents from degrading when the context fills up — retrieval-based selection, compression strategies, multi-agent isolation, and the numbers behind each choice.
Prompt Caching: Cutting Costs 50-90% Without Changing Model Behavior
How Anthropic and OpenAI prompt caching works, what breaks the cache, and how to structure prompts to hit 80%+ cache rates in production.
Prompt Versioning and CI/CD for LLM Changes
How to treat prompts as versioned, tested artifacts and build a deployment pipeline that gates every change on automated evaluation before it reaches users.
LLM-as-Judge: Calibrating Your Automated Evaluator
How to build an LLM judge that actually agrees with your domain experts — covering bias failure modes, rubric design, calibration targets, and production cost math.
Designing Tool Schemas That Models Actually Understand
How to write tool names, descriptions, and parameter schemas that prevent wrong tool selection, hallucinated arguments, and silent agent failures in production.
Few-shot prompting, chain-of-thought, and reasoning models
Master few-shot prompting and chain-of-thought, then learn why both techniques must be rethought entirely for o3, Claude extended thinking, and Gemini 2.x.
Calling Vision APIs in Production: GPT-4o, Claude, and Gemini Compared
Calling vision APIs in production: image tokens and cost math, resolution tradeoffs, OCR accuracy, and how GPT-4o, Claude, and Gemini actually compare.
Prompting reasoning models: what changes and what breaks
The counter-intuitive rules for prompting o3, DeepSeek-R1, and Claude extended thinking — simpler prompts often outperform elaborate ones, and common techniques backfire.
JSON Mode vs Structured Outputs vs Tool Calling: What Actually Differs
Three APIs that all return JSON but with very different guarantees. Understand when each breaks, what they cost, and which to reach for in production.
System Prompts: Design Patterns That Actually Hold Up in Production
Four-section structure, cache-friendly ordering, modular composition, and how to write system prompts that survive model upgrades and adversarial users.
Prompt Injection and Jailbreaks: Anatomy of the Number One LLM Threat
How direct, indirect, and multimodal prompt injection attacks work in 2026 — and the five-layer defense architecture that actually reduces them in production.
The Decision Ladder: Fine-Tuning vs RAG vs Prompting
A concrete decision framework for when prompting, RAG, or fine-tuning pays off — grounded in real cost, latency, and data trade-offs, not vendor marketing.
From Prompt Engineering to Context Engineering: What Actually Changed
Why writing clever prompts is the wrong mental model for 2025 LLM systems, and how the context-window-as-RAM framing changes what you build.
Context Engineering: Filling the Window Right
The shift from prompt to context engineering: how to write, select, compress, and isolate what goes in the window — and the silent failure modes that ship to production.
Reasoning Models: When and How to Use Them
Reasoning models spend inference tokens thinking before they answer. Learn when that's worth it, when it's a waste, how prompting changes, and how to control cost.