RAG & LLMOps
Technical articles to build, evaluate and maintain RAG systems and AI assistants in production.
Taking an AI assistant from POC to production is the real challenge. These articles cover RAG architecture, quality evaluation, chunking, monitoring, security and the LLMOps practices required to industrialize generative AI.
12 articles in this cluster
'Go production' checklist for an AI assistant
Before deploying your AI assistant to production, check these 25 points: answer quality, security, monitoring, compliance, scalability. The complete checklist for a calm and controlled move to production.
Deploying a RAG chatbot on your internal documents
Step-by-step guide to deploying a RAG chatbot on your document base: architecture, tool choices, costs, timelines and pitfalls to avoid.
RAG security: access rights and information leakage
A RAG without access control potentially exposes all documents to all users. Confidential information leakage, rights bypass, prompt injection: the security risks of RAG and how to mitigate them.
Logging and audit: making AI traceable
The AI Act requires the traceability of AI systems. Structured logs, audit trail, retention and access: a complete guide to making your AI assistant auditable by regulators, auditors and your internal teams.
Non-regression tests for an AI assistant
A prompt change, a new model, an index update: each modification can break your AI assistant. A non-regression testing protocol specific to LLM applications, with tools and CI/CD automation.
LLMOps: monitoring quality, costs and latency
An LLM in production without monitoring means a bill that climbs silently and quality that degrades without alert. Observability, alerts, dashboards: the LLMOps guide to staying in control.
Citations: how to force a sourced answer
An AI answer without a source is an opinion, not information. Prompt engineering, post-processing and RAG architecture techniques to guarantee that every claim is traceable to an identified document.
Indexing without noise: cleaning, deduplication, metadata
Headers, footers, duplicates, scanned PDFs: noise in your vector index degrades the quality of your RAG. A complete guide to cleaning, deduplication and metadata enrichment for clean indexing.
Chunking: how to split your documents (guidelines)
60% of a RAG's quality depends on the quality of chunking. Chunk size, overlap, semantic vs syntactic strategies: a practical guide to splitting your documents and maximizing result relevance.
Evaluating a RAG chain: metrics and protocol
Faithfulness, relevance, recall: the essential metrics for measuring the quality of a RAG chain in production. Complete protocol, open source tools and recommended thresholds to guarantee reliable answers for your users.
RAG vs fine-tuning: how to decide
RAG or fine-tuning? The answer depends on your use case. Decision criteria, pros and cons of each approach, and when to opt for a hybrid strategy.
Democratized fine-tuning: customize your AI for €50
Fine-tuning AI models is no longer reserved for tech giants. With costs dropping to 50 euros and accessible tools, SMBs can now customize an LLM for their specific business. An overview of the options and the method.