If you don’t want to reinvent all of this yourself, this is exactly the problem we’re solving at Ailog.
Most local LLM setups break down because people try to use the model as both the reasoning engine and the memory store. That doesn’t scale. What works in production is a layered approach: external long-term memory (vector DB + metadata), short-term working state, aggressive summarization, and strict retrieval and evaluation loops.
That’s what we built at https://www.ailog.fr
. We provide a production-ready RAG stack with persistent memory, retrieval controls, grounding checks, and evaluation tooling so models can handle long-horizon, multi-step tasks without blowing up the context window. It works with local or hosted models and keeps memory editable, auditable, and observable over time.
You can still build this yourself with Ollama, Chroma/Qdrant, and a custom orchestrator, but if you want something already wired, tested, and scalable, that’s the niche we’re filling.
Happy to answer questions or share architecture details if useful.
Before everything i want to tell you that i am working on a RAG project and you can check https://www.ailog.fr and our app https://app.ailog.fr/ .
You can check it out if you want a production-ready RAG ( we have an API and we can scale to enterprise level if necessary ).
Next for the feedback part :
Evaluate LLM systems as three separate layers: model, retrieval or grounding, and tools. Measure each with automated tests plus continuous human sampling. A single accuracy metric hides user frustration. Instrument failures, not just averages.
Practical framework you can implement quickly:
Human in the loop: Review 1 to 5 percent of production sessions for correctness, safety, and helpfulness. Train a lightweight risk flagger.
Synthetic tests: 100 to 500 canned conversations covering happy paths, edge cases, adversarial prompts, and multimodal failures. Run on every change.
Retrieval and hallucinations: Track precision at k, MRR, and grounding coverage. Use entailment checks against retrieved documents.
Tools and integrations: Validate schemas, assert idempotency, run end to end failure simulations. Track tool call and rollback rates.
Telemetry and drift: Log embeddings, latency, feedback, and escalations. Alert on drift, hallucination spikes, and tool failures.
Weekly metrics: correctness, hallucination rate, retrieval precision at 5 and MRR, tool success rate, CSAT, latency, escalation rate.
Pilot plan: one week to wire logging, two weeks to build a 100 scenario suite, then nightly synthetic tests and daily human review.
You can check out https://app.ailog.fr/en/tools to get some insight on way to evaluate your RAG, we have free tools here for you to check and use :)
Most local LLM setups break down because people try to use the model as both the reasoning engine and the memory store. That doesn’t scale. What works in production is a layered approach: external long-term memory (vector DB + metadata), short-term working state, aggressive summarization, and strict retrieval and evaluation loops.
That’s what we built at https://www.ailog.fr . We provide a production-ready RAG stack with persistent memory, retrieval controls, grounding checks, and evaluation tooling so models can handle long-horizon, multi-step tasks without blowing up the context window. It works with local or hosted models and keeps memory editable, auditable, and observable over time.
You can still build this yourself with Ollama, Chroma/Qdrant, and a custom orchestrator, but if you want something already wired, tested, and scalable, that’s the niche we’re filling.
Happy to answer questions or share architecture details if useful.