AI Integration
A Reranker Is a Second Opinion on Retrieval: When the Extra Pass Is Worth Its Latency
First-stage retrieval is built for recall at speed. A reranking pass trades milliseconds for precision, and whether that trade pays off is something you can measure.
Where You Put Things in a Long Prompt: Placement of Documents, Instructions, and the Question
With large inputs, the order of the pieces is a prompt-design decision you can test, not a formatting detail.
Embeddings Are Versioned Data: Planning for the Day You Change the Embedding Model
A vector index is only meaningful relative to the model that produced it. Treat the model, dimensions, and preprocessing as part of the schema.
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.
Chunk Retrieval Documents by Structure First, Then Tune the Size With a Measurement
Fixed 500-token windows are a fine baseline and a poor ending point. Splitting along the document's own structure and checking the result against real questions usually beats any amount of overlap tuning.
Deadline Propagation: Budgeting Time Across Every Stage of a Multi-Call LLM Pipeline
A per-call timeout tells you nothing about whether the whole request will finish in time. Pass one deadline down the pipeline and let each stage spend only what is left.
Checking Citations Mechanically: Verifying That a RAG Answer's Quotes Really Exist in the Source
If your model quotes its sources, you can verify the quotes with ordinary string matching, which catches a whole class of fabrication for almost no cost.
Client-Side Rate Limiting for LLM APIs: Token Buckets, Backpressure, and Fair Sharing
Waiting for a 429 and retrying is a recovery strategy, not a capacity plan. Here is how to stay under provider limits on purpose.
Truncated Output Is a Silent Failure: Handling Stop Reasons and Length Limits
A response cut off at the token limit still returns HTTP 200. Here is how to read stop reasons, handle truncation, and track your truncation rate.
Streaming LLM Responses Is a UX Problem: Designing Around Time-to-First-Token and Mid-Stream Failure
Turning on streaming makes a feature feel faster, but it also creates new states and failure modes that need deliberate design.
Negative Examples in Prompts: When Showing the Model What Not to Do Backfires
Telling a model what to avoid can anchor it on exactly that behavior. When negative examples help, when they hurt, and what to write instead.
Log Enough to Replay a Bad LLM Response, Not Just to Know It Happened
Most LLM logging records that a call occurred. Debugging requires recording enough to reproduce it. Here is the minimum replay record and how to keep it safe.
Designing a Multi-Provider Model Router: Fallback Chains, Not Just a Single Switch
Routing between model providers for cost or redundancy sounds like a load-balancer problem. The failure modes say otherwise.
The Hidden Cost of Retries: Why "Just Retry the LLM Call" Isn't Free
Retries add load, multiply across layers, and re-bill tokens. Here is a retry policy built on AWS guidance and adapted to LLM calls with side effects.
Silent Model Upgrades Are a Production Risk — Here's How to Guard Against Them
Models have a lifecycle that ends in retirement, and replacements change behavior. Here is how to pin, log, test and canary your model versions.
Multi-Turn State: What Breaks When Your AI Feature Moves From Single-Shot to Conversational
The API remembers nothing, so your code owns the conversation. Here is what accumulates, what breaks, and what to decide on purpose.
A Framework for Deciding Where an LLM Call Belongs in Your Request Path
Sync, streaming, or background job — the placement decision shapes your product more than the prompt does.
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.
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.