Prompt Engineering
Every technique that works — with real examples
10 min readAdvanced Prompt Engineering: Chain-of-Thought, ReAct, and Few-Shot Patterns
Maximize output quality by applying structured reasoning pathways and agentic planning frames directly inside prompts.
8 min readSystem Prompt Design: Securing LLM Applications Against Jailbreaks
Protect your proprietary guidelines and user data from prompt injection attacks. Learn how to write secure, leak-proof system instructions.
9 min readMetaprompting: Using LLMs to Write Better Prompts Automatically
A guide to building feedback loops where an LLM critiques and optimizes prompt instructions iteratively to hit exact performance targets.
8 min readStructured Outputs from LLMs: Leveraging JSON Mode and Tool Calling
How to guarantee LLMs conform to schema specifications for databases and APIs using instructor libraries and native compiler features.
10 min readPrompt Versioning and Evaluation in CI/CD Pipelines: A Practical Guide
Treating prompts as code: how to track prompt changes, version them in git, and run automated regression tests on code changes.
9 min readPrompting for Translation: Context-Aware Output That DeepL Cannot Match
Where LLMs outperform traditional MT tools on translation tasks - tone, idioms, domain context - and where they fall short, plus practical techniques including style guides, glossaries, audience specs, and back-translation QA.
9 min readPrompt Engineering for SQL: How to Generate Reliable Database Queries with LLMs
LLMs can generate SQL queries from natural language, but the reliability gap between simple and complex queries is significant. Here is how to close it.
10 min readContext Stuffing vs RAG: When to Put Everything in Context
A practical decision framework for choosing between context stuffing and retrieval-augmented generation - covering token economics, chunking strategy, hybrid approaches, and a cost comparison between stuffing 500 pages versus retrieving 5 chunks.
9 min readPrompting for Agents: System Prompts That Actually Control Agent Behavior
How agent system prompts differ from chatbot prompts - tool descriptions, invocation criteria, output format for tool calls, stopping conditions, safety constraints, ReAct format, handling uncertainty, and common failure modes.
6 min readLLM Output Parsing: How to Reliably Extract Structured Data from Model Responses
Getting consistent structured output from LLMs requires more than asking nicely for JSON. Here is the reliability spectrum and how to reach 99% parseable output with concrete code examples.
8 min readConstitutional AI Prompting: How to Make Models Critique and Revise Their Own Outputs
Constitutional AI gives models a set of principles to evaluate their own outputs. You can apply the same pattern in your prompts to improve quality.
9 min readPrompting Misconceptions: What Does Not Work Despite the Hype
A research-backed examination of prompting techniques that underperform their reputation - chain-of-thought on simple tasks, longer prompts, role prompting, threats, and jailbreaks - and what actually works instead.
8 min readPrompting for Code Review: Getting Useful Feedback, Not Compliments
How to write prompts that produce actionable code review - specifying focus areas, severity tiers, concrete fix examples, adversarial review framing, and the difference between diff review and full-file review.
9 min readPrompt Patterns for Customer Support AI: What Works and What Creates Liability
Customer support AI fails in predictable ways. The right system prompt prevents most of them. Here are the patterns that work and the mistakes that create problems.
9 min readPrompt Testing Methodology: A Systematic Approach for Teams
How to test prompts systematically - defining test sets and success criteria, building golden datasets for regression testing, A/B testing in production, statistical significance, and the minimum viable setup for small teams.
8 min readChain of Density Prompting: How to Get Information-Dense Summaries from LLMs
Chain of Density produces better summaries by iteratively densifying a sparse draft. Each pass adds missing information without increasing length. Here is how it works.
9 min readSystem Prompt Security: Protecting Against Extraction and Injection Attacks
A practical guide to system prompt security - understanding extraction and injection attacks, defense layers that actually work, and the fundamental truth that system prompts cannot be cryptographically secured.
9 min readPrompt Versioning: How to Manage Prompts in Production Without Breaking Things
Prompts in production need versioning, testing, and rollback capability just like code. Here is the system that prevents silent regressions when you improve a prompt.
9 min readFew-Shot Example Selection: How to Choose Examples That Actually Help
The science of choosing few-shot examples - diversity, representativeness, ambiguity, relevance, dynamic selection, negative examples, ordering effects, and the empirical 3-5 example sweet spot from Brown et al. 2020.
8 min readNegative Prompting: How to Break LLMs Out of Their Default Patterns
Models default to generic structures, hedging phrases, and formulaic openings. Negative prompts tell them what not to do and break those patterns effectively.
8 min readReAct Prompting: How to Make LLMs Reason and Act in Alternating Steps
ReAct prompting alternates Thought, Action, and Observation steps so the model commits reasoning before choosing an action. Here is how to use it effectively in 2026.
8 min readPrompt Compression: How to Cut Token Costs 40-60% Without Losing Output Quality
Compressing prompts reduces token costs without degrading output quality. These techniques can cut your prompt length by 40-60% with the same results.
8 min readPrompting for Classification: Getting Consistent Labels Every Time
How to write prompts that produce reliable, consistent classification labels - covering category definitions, JSON output, multi-label vs single-label, confidence scores, and when to use zero-shot vs few-shot vs fine-tuning.
6 min readPrompting for Data Extraction: Structured Output From Unstructured Text
A complete guide to extracting structured data from text with LLMs - field definitions, JSON schemas, function calling for guaranteed structure, missing field handling, batch efficiency, and accuracy limits.