LLM & RAG Application Engineering
LLM features that are grounded, evaluated, and safe to ship.
The problem
LLM demos are easy; trustworthy LLM products are hard. Without retrieval grounding, evals, and guardrails, features hallucinate, leak context, and erode user trust the moment they hit real data.
What you get
A production RAG or LLM feature with hybrid retrieval, citation grounding, an evaluation harness that catches regressions, and guardrails that keep answers accurate and on-policy.
What's included
- Retrieval pipeline design with chunking, embeddings, and hybrid search
- Citation grounding so answers are traceable to sources
- Prompt and context engineering with versioning
- Evaluation harness for accuracy, groundedness, and regressions
- Hallucination mitigation and guardrails
- Optional fine-tuning and cost/latency optimization
Typical stack
PythonLangChainLangGraphHugging FaceOpenAI APIVector DBspgvector
Frequently asked questions
What is a RAG system?
Retrieval-Augmented Generation (RAG) pairs a language model with a retrieval layer that fetches relevant, up-to-date context from your own data at query time. The model answers from that retrieved context, which improves accuracy and lets answers cite their sources.
How do you reduce hallucinations in LLM features?
Through grounding answers in retrieved sources, requiring citations, constraining outputs with guardrails, and running an evaluation harness that measures groundedness so regressions are caught before release rather than by users.
Do I need to fine-tune a model?
Often not. Retrieval and prompt engineering solve most use cases more cheaply and flexibly than fine-tuning. Fine-tuning is recommended only when there's a clear, measured gap that retrieval can't close.
Ready to get started with llm / rag engineering?
Tell me about your project and I'll come back with ideas, a clear scope, and next steps — usually within 24 hours. Free discovery call, no commitment.