/ai--ai-integration

Scaffolds a chatbot on your existing stack: streaming responses, persistent conversation history, optional RAG grounding and a feedback loop.

Audits what goes into your AI app's context window and decides what to retrieve, summarize or compact, then checks the result with an eval.

Implements vector embeddings in your app for semantic search, RAG or similarity matching: ingestion, chunking, querying and relevance tuning.

Decides honestly whether an LLM needs fine-tuning or better prompts, and if so prepares data, trains and compares before and after on held-out data.

Adds input and output filters, prompt-injection defense and personal-data handling around an AI feature, then proves them with adversarial tests.

Measures where your app spends tokens, then applies model routing, caching and prompt compression, keeping each change only if an eval shows quality holds.