Custom LLM operation and review
Make your LLM service easier to check and maintain
Use these guides to decide how answers are reviewed, documents stay current and changes reach production. Each covers a practical operating decision, including what to record, who should act and how to check the result.
Practical LLM operations guides
Each guide includes a decision to make, specific operating checks and related reading for the next part of your process.
Decide when your business AI should decline to answer
Define answer boundaries, useful abstention wording and owned human-review tasks for a business knowledge assistant.
Read the guideCheck whether AI source citations support the answer
Review business AI answers for source support, accessible references, version accuracy and unsupported additions.
Read the guideControl document versions in a business AI knowledge base
Define current sources, retired documents, ownership and update checks so a knowledge assistant uses the intended business instructions.
Read the guideTest who can access information through your LLM
Plan permission tests for a business LLM using fictional records, separate user roles and evidence of both allowed and denied access.
Read the guideCheck an LLM release against the work it already handles
Build a practical regression review for changes to an LLM application, with task examples, acceptance criteria and a controlled rollback decision.
Read the guideTurn employee LLM feedback into reviewed improvements
Set up an owned feedback queue for incorrect or unhelpful LLM answers, with useful evidence, source review and verified closure.
Read the guideRespond to an unexpected rise in business AI costs
Define a cost-spike response that checks actual usage, contains affected work and preserves evidence before changing a business AI service.
Read the guidePrepare an exit plan for a business AI provider
Plan what must transfer, what needs rebuilding and how to test continuity before replacing a business AI provider.
Read the guideRemove retired documents from an AI knowledge service
Plan how retired documents stop appearing in business AI answers, with source checks, retrieval tests and clearly stated retention limits.
Read the guideEarlier reading
Data sovereignty: earlier reading
Background discussion of data location and control. Verify current provider terms and applicable requirements for your proposed setup.
RAG and fine-tuning: planning the approach
Earlier reading on two ways to adapt an AI service. Test options against your own task and source material before choosing an implementation.
Scope your next LLM improvement
Describe the task, source systems and current limitations. We can discuss evaluation criteria, access requirements and the scope of a controlled pilot. Please leave confidential documents and credentials out of this enquiry.