RAG Chunking Strategies and Chunk Size Trade Offs for Technical Docs
For technical documentation in a retrieval augmented generation system, split text along its natural structure, such as headings, sections, and code blocks,...
Category
AI
AI Agent Guardrails and Output Filtering for Customer Facing Assistants
Guardrails for a customer facing AI assistant work best as layers: restrict what the system can do through tool permissions, check inputs and outputs for...
LLM Evaluation Datasets and Regression Testing for Production Prompts
To change a production prompt or model safely, build an evaluation dataset of realistic inputs with expected outcomes, score outputs automatically, and run the...
AI Agent Tool Schema Minimization for Lower Token Usage
Large tool descriptions consume context even when tools are never invoked. Start with a clearly defined correctness invariant and workload. Identify the state...
RAG Citation Verification Against Retrieved Source Passages
An LLM can generate plausible citations that do not support the claim being made. Start with a clearly defined correctness invariant and workload. Identify the...
Embedding Model Versioning and Reindexing Strategies for RAG Systems
In a RAG system, vectors created by different embedding models cannot be compared with each other, so changing the model means reindexing your data. The safest...
LLM Structured Output Validation and Retry Patterns for Backends
LLM structured output should never be trusted just because you asked for JSON. Validate every response against a schema, retry a limited number of times with...
LLM Prompt Caching Strategies for Cost Reduction in Production
Prompt caching reduces LLM cost and latency by reusing the processing of a repeated prompt prefix instead of recomputing it on every request. To benefit, put...
AI Agent Retry Budgets for Multi Step Tool Workflows
Unbounded agent tool retries can compound outages and repeat unintended side effects. Reliable applications must preserve explicit correctness guarantees under...