AI Cheat Sheets

One-page AI cheat sheets for developers: how each mechanism works, the rules that hold, and when to use it. Click a sheet to watch it build step by step.

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Agentic RAG cheat sheet

An agent loop only pays off on top of retrieval that already works, and only when it has a hard stopping rule.

→ Topic: Agentic RAG

AI Documentation Generation cheat sheet

Generated docs stay trustworthy only when every symbol is verified and the docs regenerate when code changes.

→ Topic: AI Documentation Generation

Bias and Fairness Metrics cheat sheet

When groups have different base rates, you choose which fairness to satisfy and write down the trade-off.

→ Topic: Bias and Fairness Metrics

Continuous Batching cheat sheet

Finished requests free their slot at once, so the GPU stops waiting for the slowest request in the batch.

→ Topic: Continuous Batching

Data Poisoning cheat sheet

Training-time attacks need retraining to fix; RAG and agent memory stay open after deployment.

→ Topic: Data Poisoning

Data Versioning cheat sheet

A pointer in Git plus bytes in cheap storage rebuild any past dataset, but the hash cannot tell good data from bad.

→ Topic: Data Versioning

LLM Load Testing cheat sheet

Under concurrent load, the first token waits for a KV-cache slot, so the p99 tail breaks long before the average moves.

→ Topic: LLM Load Testing

Multi-Vector Retrieval cheat sheet

Keeping one vector per token recovers nuance a single embedding averages away, but the index grows accordingly.

→ Topic: Multi-Vector Retrieval

Reward Model Architecture cheat sheet

The score only ranks answers, and the policy compounds every bias in it until it learns to game the score.

→ Topic: Reward Model Architecture

Tree of Thoughts cheat sheet

Branching beats a single chain only when the evaluator can tell a good partial step from a bad one.

→ Topic: Tree of Thoughts