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LLM Engineering

RAG Pipelines That Hold Up in Production

Retrieval-augmented generation looks easy in a notebook. This article covers what changes at production scale: vector search, evaluation, latency, and governance guardrails.

HavocLLP TeamEngineering team1 min read

  • LLM Engineering
  • RAG
  • Vector Search
  • LLM Evaluation

LLM Engineering

HavocLLP / Field note
Fig. Retrieval connecting models to internal knowledge.

Article

Retrieval-augmented generation connects large language models to internal knowledge bases for accurate, context-aware responses. The demo version fits in a single script. The production version needs vector search that stays fast as the corpus grows, retrieval quality that is measured rather than assumed, and guardrails that handle the realities of enterprise data.

The four production concerns

  1. Advanced vector search with embeddings tuned to the domain, not generic defaults.
  2. Contextual accuracy: retrieved passages must actually answer the question asked.
  3. Latency optimisation across embedding, retrieval, reranking, and generation.
  4. Retrieval evaluation as a standing practice, rerun whenever data or models change.
Fig. Embed, retrieve, rerank, generate: the RAG path.

Evaluate against your own data

Benchmarking foundation models against your domain data is how you select and optimise the right model for the use case. Generic leaderboards do not predict performance on internal documents, product catalogues, or support histories. Build a small, representative evaluation set early and keep it green with every change.

yaml
retrieval:
  embeddings: domain-tuned
  top_k: 8
  rerank: true
evaluation:
  dataset: internal-qa-v1
  run_on: [data-change, model-change]
guardrails:
  pii_protection: true
  prompt_injection_defence: true
  role_based_access: true

Governance is part of the architecture

Secure deployments need PII protection, prompt injection defence, and role-based access controls from the start. These are easier to design into a RAG architecture than to retrofit after a security review. Our LLM services cover RAG design, fine-tuning, Genie workspaces, security, and evaluation as one delivery line.

We approach LLM deployment as a production engineering challenge, designing robust RAG architectures and building governance guardrails from the ground up.

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HavocLLP TeamEngineering team1 min read

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