Design and industrialization of generative AI applications relying on retrieval-augmented generation (RAG): corpus mapping and cleaning, vector indexing, LLM orchestration, privacy guardrails, logging, quality benchmarks, and regular audits.
Generative language models (Claude, GPT, Gemini, Mistral, Llama) are remarkable on general knowledge but limited on specialized and proprietary corpora: internal documentation, contracts, product sheets, ticket bases, case law. Retrieval-augmented generation (RAG) bridges this gap: we retrieve relevant passages from an indexed document base, provide them to the model in the prompt, and the model generates a sourced response based on the returned passages. This pattern solves three problems at once: eliminating hallucinations on the business corpus, maintaining source traceability, and enabling fast updates without retraining the model. Typical enterprise use cases are search in internal documentation, customer support assistance, document processing (extraction, qualification, comparison), and leveraging editorial heritage (media, telecoms, public sector).
A RAG POC typically works in two weeks: vector indexing, prompt template, cloud LLM. But the move to real production reveals several gaps. First, quality: a retrieval that works on ten demo questions does not hold up on a thousand real user queries. Second, security: the POC sends the entire corpus to the model without filtering, which is not sustainable against sectoral requirements. Third, observability: without logging and continuous benchmarking, you don't know if quality is drifting. Finally, inference costs explode at volume if the pipeline is not optimized. Our AI, Data, and Automation expertise specifically addresses this gap between POC and production, framed by the ATLAS methodology.
The choice of inference model depends on the use case. For the majority of enterprise RAG cases, Anthropic Claude Sonnet 4.6 or OpenAI GPT-4o offer the best quality/cost ratio with a large context window (200k tokens and more) that simplifies retrieval. For regulated or sovereign cases, open source models (Llama, Mistral, Qwen) deployed self-hosted are possible, at the cost of heavier infrastructure. For latency-critical cases, smaller specialized models (Claude Haiku, GPT-4o-mini, Mistral Small) often suffice. The right pattern is almost never a single model: dynamic routing between models based on query complexity, aggressive caching on repeated prompts, fallback to a lighter model in case of load spikes.
Proprietary document base (PDF, Word, intranet, ticketing), business need for AI-augmented search
Default choice. Claude excels at reasoning and instruction following, pgvector avoids external dependency, custom orchestration (Python or Node) remains simple to maintain and evolve. Recommended approach for cloud-agnostic or multi-cloud organizations.
Organizations committed to Microsoft Azure. Fully managed and governed stack, native integration with Microsoft Purview, Microsoft Compliance Manager compliance, easy access to GPT-4o and o1 models.
Organizations committed to AWS. Bedrock multi-model (Claude, Llama, Mistral, Cohere), OpenSearch for vectors, Step Functions for orchestration, governance via AWS IAM and CloudTrail.
Organizations committed to Google Cloud. Gemini directly accessible, AlloyDB for vectors, Vertex AI Pipelines for orchestration and continuous evaluation.
A GenAI RAG project in production is generally structured over three to nine months depending on corpus complexity and governance requirements. For a circumscribed use case (internal documentation, customer support) with a corpus of a few thousand documents, plan three to five months with a cell of four people: an AI architect, a senior RAG engineer, a full-stack developer, a business referent assigned at 30%. For ambitious programs with multi-use cases, AI Act governance, continuous monitoring, plan six to nine months and a cell of six to eight people including a data scientist and a legal or compliance referent.
Underestimating the quality of the source corpus. A RAG does not correct corpus errors, contradictions, or duplicates — it amplifies them. A poorly prepared corpus leads to a system that hallucinates while claiming to be sourced.
Systematic corpus audit at project start: deduplication, detection of outdated versions, identification of internal contradictions, classification by reliability. Critical documents are annotated and certified by a business referent. The ingestion pipeline integrates quality rules (minimum length, detected language, mandatory metadata) and a rejection registry periodically reviewed. See the ATLAS methodology.
Confusing RAG POC and RAG in production. The POC works on ten demo questions; in production, you discover ambiguous queries, multi-turn questions, prompt injection attacks.
Quality bench built upstream, on representative test sets (fifty to two hundred questions from real logs or user interviews). The bench covers faithfulness, context relevance, answer relevance. It is run at every pipeline modification and continuously in production on a sample. No major deployment without measured improvement on the bench.
Neglecting privacy guardrails. Sending business corpus to the model without filtering exposes personal data or trade secrets, violating Bill 25, GDPR, or client contracts.
Explicit redaction layer between retrieval and LLM: automatic PII detection, configurable masking, allowlist of authorized fields, leak audit through adversarial tests. For ultra-sensitive cases, on-premise or sovereign cloud deployment with self-hosted model (Llama, Mistral). All prompts and responses are logged with their sensitivity level.
Going to production without observability. Without logs, traces, and monitoring, you don't see quality drift, user abuses, or cost spikes.
Observability stack from day 1: prompt and response logging (LangSmith, Langfuse, or custom solution), distributed traces (OpenTelemetry), cost dashboards, alerts on latency and error rates. Weekly drift reviews, automatic alerts on budget overruns or recurring failures.
Ignoring costs at scale. The POC costs thirty euros per day; at one thousand users, one thousand requests per day, this can reach several thousand euros monthly if nothing is optimized.
Cost strategy from design: aggressive caching (Anthropic prompt cache, custom semantic cache), dynamic routing between models based on complexity, batch on non-real-time tasks, optimized prompts (token reduction). Monthly estimate revised at each milestone, alerts on overruns, model choice adapted to each task (Haiku or GPT-4o-mini for simple tasks, Sonnet or GPT-4o for complex reasoning).
NLP and human-in-the-loop platform for semantic qualification of editorial content for a national telecom operator. Tagger rules, continuous lexicon learning, vector indexing (pgvector), Claude models for reasoning, logging, and editorial quality dashboard.
Program capability: AI agent platform for document processing (clause extraction, compliance check, pre-validation). RAG pipeline, agents specialized by typology, human validation interface. Reference framing we know how to set up.
It depends on corpus maturity, governance requirements, and volume. For a circumscribed use case (internal documentation, customer support, enriched FAQ), plan three to five months with a cell of four people to go from POC to production. For an ambitious program with multi-use cases, AI Act governance, and continuous monitoring, plan six to nine months with a team of six to eight people. Duration depends mainly on upstream corpus quality: a corpus already cleaned and cataloged accelerates the project by at least two months.
The choice depends on the use case and governance constraints. For the majority of enterprise RAG cases, Claude Sonnet 4.6 or GPT-4o offer the best quality/cost ratio thanks to their large context window. Gemini is relevant in the Google Cloud ecosystem. For regulated or sovereign cases, open source models (Llama 4, Mistral, Qwen) deployed self-hosted are possible but require heavier infrastructure. In practice, a mature RAG system combines several models: a large model for complex reasoning, a small model for simple tasks (classification, extraction), with dynamic routing.
Three pillars. First, a quality bench built upstream with fifty to two hundred reference questions, run at every pipeline modification and continuously in production on a sample. Standard metrics are faithfulness (is the response faithful to the retrieved passages?), context relevance, and answer relevance. Second, a human-in-the-loop (HITL) on critical cases to validate sensitive outputs. Third, complete production observability that detects drifts in real time: new question classes, hallucination rate, latency, costs.
Two complementary approaches. The first is pipeline-side redaction: before sending retrieved context to the model, automatic filtering of personal information, secrets, and sensitive fields is applied, with an explicit allowlist of authorized fields. The second is choosing an adapted deployment: for ultra-sensitive cases, a self-hosted model in sovereign cloud or on-premise (Llama, Mistral) guarantees that no data leaves the perimeter. Anthropic and OpenAI also offer contractual commitments on non-use of data for training, which are necessary but not always sufficient depending on the sector.
In the majority of enterprise use cases, RAG is preferable to fine-tuning. It is simpler, less expensive, easier to update (adding a new source = enriching the index, not retraining), and more traceable (cited sources are visible). Fine-tuning has its place to adjust the style or tone of a model, or for cases where retrieval is not enough (for example, internalizing a very repetitive specific logic). In practice, many ambitious projects combine both: a light fine-tune on style, a RAG for factual knowledge.
The AI Act classifies AI systems by risk level. A RAG assistant for editorial drafting support is typically limited risk, which imposes transparency toward users (indicating they are interacting with an AI system) and documentation of sources and limits. A RAG system used in high-impact decisions (recruitment, credit, healthcare) shifts to high risk, which imposes a conformity assessment, human supervision, logs, and robustness controls. The ATLAS methodology integrates this classification from the scoping phase and conditions the level of governance deployed accordingly.
The cost of a production RAG splits into build and run, and each answers to different variables. For build: corpus size and heterogeneity, and above all the quality of document extraction and chunking, which dominates the effort. For run: daily query volume and model choice, plus vector database hosting. A self-hosted variant shifts cost from run to hardware investment and operations staffing. Free initial scoping call, 30 minutes to 2 hours.
Five practices we apply on every production RAG: (1) hybrid retrieval (semantic via embeddings + keyword via BM25, then re-rank) — pure semantic always misses exact-term queries. (2) Adaptive chunking by document type — legal contracts and product specs need different chunk sizes. (3) Source citation in every answer — required for trust and auditability. (4) Continuous evaluation with ragas or a custom bench, run on every model or prompt change — RAG quality drifts silently otherwise. (5) Pre-LLM filtering of personal data — pseudonymize before sending to the model. Beyond these: regional hosting for compliance (Quebec Law 25, GDPR), human-in-the-loop checkpoints on high-stakes decisions, and FinOps monitoring of LLM token spend. See GenAI RAG in production journey.
We frame the trajectory, the budget, and the deliverables in a first thirty-minute conversation. A short POC can be proposed before committing to the full program.
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