AI Tools
Rewrite the Question Before You Retrieve: Query Rewriting for RAG That Doesn't Change What the User Meant
Users ask follow-ups, use pronouns, and write in fragments. A small rewriting step before retrieval can fix recall, if you constrain it and test it on its own.
Temperature Zero Is Not Determinism: What You Can and Can't Reproduce in LLM Calls
Setting temperature to zero narrows output variation but doesn't guarantee identical responses. Here is where the remaining variation comes from and how to build around it.
Conversation History Compaction: Rolling Summaries That Don't Lose Commitments
A long chat or agent session eventually outgrows its context. How you compress it decides whether the model remembers what it promised.
Delimiters Are Cheap Insurance: Separating Instructions, Data, and Examples in a Prompt
A prompt that mixes your instructions with the text it should process invites misreadings. Consistent section markers fix most of them for almost no cost.
Idempotency Keys for Agent Tool Calls: Making Retries and Replays Safe
Agents retry, resume and repeat themselves. Here is how idempotency keys, modeled on payment APIs, keep side-effecting tool calls safe.
Building a Shared Prompt Library Without It Turning Into Tribal Knowledge
Once more than one person is writing prompts for the same product, copy-paste drift sets in fast. A few concrete conventions keep a prompt library usable instead of becoming a graveyard of near-duplicate files.
Handling PDFs, Images, and Other Multimodal Inputs in an LLM Pipeline
Piping raw files straight into a vision-capable model works in a demo and falls apart in production. Separating extraction from understanding — and caching the extraction — is what makes multimodal features debuggable.
The Case for Synthetic Adversarial Personas in Your Eval Suite
Most eval sets are built from happy-path examples and yesterday's bugs. Neither one tells you how the system fails against a user trying to break it.
What to Look for in an LLM Observability Tool Before You Buy One
Pick LLM observability tooling by the failures you will actually have: trace completeness, per-step cost, stop reasons, version linkage, replay and privacy.
A Practical Rubric for Choosing Between Few-Shot Prompting and Fine-Tuning
Prompting, retrieval and fine-tuning fix different problems. Here is OpenAI's diagnostic framework plus rules of thumb for choosing between few-shot and fine-tuning.
What to Cache — and What Never to Cache — in an LLM-Backed Feature
Caching feels like a free win for any expensive model call, but applied carelessly it turns a probabilistic system into a source of silent, confidently wrong answers.
Model Context Protocol (MCP): A Universal Adapter for AI Tool Integration
MCP is an open standard for connecting AI applications to tools and data. Here are the roles, the features, and the security rules in the current specification.