Production-ready LLM workflows hinge on three moves: robust agent evaluation [1]. RAG-as-a-Service for local models [2] helps private data stay in-house. Code-execution integration via MCP [3] closes the loop.
Evaluation platforms • Langfuse — supports comprehensive tracing with full self-hosting control [1]. • Maxim — offers multi-turn simulations across tool use and API calls, plus real-time alerts [1]. • Arize — provides production monitoring with drift detection and enterprise compliance [1]. • Braintrust — LLM proxy for logging and an in-UI playground for rapid iteration [1]. • Comet Opik — unifies LLM evaluation with ML experiment tracking [1].
RAG as a Service for local models llama-pg is an open-source RAG-as-a-Service orchestrator that automates embeddings across projects while keeping data private [2]. It teams with LlamaParse for parsing and TimescaleDB’s pgai for vectorizing; deployment is via Docker Compose or Helm for Kubernetes [2].
Linking LLMs to code execution environments Teams explore Anthropic MCP-style work to run and reuse code in an execution environment, using an MCP server and client patterns to connect tools like code senders and folders [3].
RAG-focused review papers A focused post catalogs recent RAG work, useful for teams weighing deployment patterns and tooling [4].
Closing thought: the playbook today blends strong evaluation, private-model RAG, and disciplined code-execution integration to move from experiments to production-ready pipelines.
Referenced POST IDs: [1], [2], [3], [4]
References
Compared 5 AI eval platforms for production agents - breakdown of what each does well
Compared five eval platforms for production LLM workflows, detailing strengths in agent evaluation, rapid prototyping, production monitoring, and open-source control.
View sourceI built a RAG as a Service orchestrator for local models
Builds llama-pg, a RAG as a Service for local models; private embeddings, OpenAI-compatible options, Docker/Helm deployment; background parsing
View sourceHow to link an AI to a code execution environment?
Discusses connecting an LLM to a code execution environment using MCP, seeks open-source solutions and implementation tips.
View sourceRAG Paper 25.11.09
List of arXiv/RAG papers on LLMs and retrieval-based QA; covers safety, regulation, context reuse, and comparisons in CS literature overview.
View source