All 50 articles, sorted alphabetically
Advanced RAG
Beyond basic top-K retrieval. Techniques for hard questions.
Read article →Agentic Prompting
Structure prompts for autonomous multi-step task completion.
Read article →Chain-of-Thought (CoT) Prompting
'Let's think step by step.' Unlocks reasoning in large models.
Read article →Chain-of-Verification (CoVe)
Model generates → generates verification questions → answers them → revises. Reduces hallucination.
Read article →Constitutional AI
Model critiques + revises its output per explicit principles. Bootstraps harmlessness.
Read article →Constrained Decoding
Force model to produce output matching a grammar. Zero-cost format guarantees.
Read article →Context Window Management
Chunking, summarization, retrieval. When your data doesn't fit.
Read article →Cost Optimization
Route by difficulty. Cache. Compress. 10-50x cost savings possible.
Read article →Emergent Abilities
Some capabilities appear only above threshold model size. Contested but influential.
Read article →Dynamic Few-Shot
For each query, retrieve most similar examples from bank. Big accuracy jump on hard tasks.
Read article →Few-Shot Prompting
Show the model the format via examples. Foundation of in-context learning.
Read article →Function Calling
Define tool schemas, model produces JSON call. Foundation of modern agents.
Read article →Grounding + Citations
Hallucination defense: model must quote provided sources with tags.
Read article →Instruction Hierarchy
OpenAI's formalization. Model trusts sources by role. Foundation of injection defense.
Read article →Long Context Prompting
Gemini/Claude/GPT-5 support 200K-2M tokens. New patterns emerge.
Read article →Meta-Prompting
Use LLM to generate/refine prompts for other tasks. Iterative prompt improvement.
Read article →Model-Specific Tricks
Each model family has quirks. Prompts that win on one may lose on another.
Read article →Multi-Agent Orchestration
Team of specialized agents. Orchestrator routes. Cheaper than one giant agent.
Read article →Negative Prompting
Explicit prohibitions. Sometimes works, sometimes highlights the forbidden.
Read article →Prompt A/B Testing in Production
Route traffic split. Measure metric. Statistical significance before shipping.
Read article →Analogical Prompting
'Think of a similar problem you've solved.' Model retrieves similar patterns.
Read article →Anatomy of a Prompt
Every modern LLM uses 3 roles. What each role does, when to use what.
Read article →Prompt Caching
APIs cache prefix tokens. 90% cost reduction on repeated system prompts.
Read article →Prompt Chaining
Multiple LLM calls, each doing one thing. Cheaper + more reliable than one mega-prompt.
Read article →Prompt Compression
Compress prompts 2-20x preserving downstream task quality.
Read article →Debate Prompting
Two agents argue different positions. Judge picks winner. Improves reasoning.
Read article →Debugging Broken Prompts
When prompt fails: minimal example, ablation, model comparison. Not vibes.
Read article →Prompt Delimiters
Separate instructions from data. Reduces injection + confusion.
Read article →Prompting for Code Generation
Language, style, tests, review. Foundation of Copilot, Cursor.
Read article →Domain-Specific Prompts
Schema, hints, few-shot examples, self-correction loop. Text-to-SQL production patterns.
Read article →Prompt Engineering Team Structure
Who owns prompts? How to review + ship. Emerging discipline.
Read article →Prompt Evaluation
How to measure prompt changes objectively. Reject vibes-based improvement.
Read article →Extraction Prompts
Given text, extract entities, relationships, key-value pairs. JSON schema-guided.
Read article →Multilingual Prompting
Cross-lingual tasks. English pivot. Translation fidelity.
Read article →DSPy
Declare task signatures. Framework compiles prompts + optimizes. No hand-tuning.
Read article →Prompt Design for Streaming UX
How prompt affects streamed output quality. First-token latency. Format for progressive rendering.
Read article →Temperature, Top-p, Top-k
Randomness parameters. When to crank, when to zero out.
Read article →Prompt Template Libraries
Reusable prompt templates. Variables. Composition. Trade-offs.
Read article →Prompt Versioning
Version-control prompts. Track eval metrics per version. Prevent silent drift.
Read article →ReAct
Model alternates Thought → Action → Observation. Foundation of agentic prompting.
Read article →Reflexion
Agent learns across attempts via natural language memory of past mistakes.
Read article →RAG
Retrieve relevant docs from vector DB, feed to LLM. Foundation of KB Q&A.
Read article →Role Prompting
'You are a senior security engineer…' Real gain or superstition?
Read article →Self-Consistency
Sample N CoT solutions, take majority answer. Boosts accuracy 5-15%.
Read article →Self-Refine
Model generates → model critiques → model rewrites. 1-3 iterations.
Read article →Structured Output
Force LLM output into machine-parseable format. Table stakes for production.
Read article →Summarization Prompts
Extractive vs abstractive. Length. Focus. Audience. Get precisely what you asked for.
Read article →Task Decomposition
Split hard problem into subproblems, solve smallest first, compose.
Read article →Tree of Thoughts
Explore multiple reasoning branches, prune, backtrack. RL-flavored prompting.
Read article →Zero-Shot Prompting
Ask the model directly, no examples. When it works, when it fails.
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