Guide

LLM and RAG Production Roadmap

A roadmap for building LLM and RAG systems from bounded workflows to retrieval, evaluation, agents, security, cost, and monitoring.

Related Wiki Pages

LLM Production Patterns RAG Retrieval-Augmented Generation Search Search, RAG, and Knowledge Systems LLM Evaluation Workflows Production Search Evaluation Agent Engineering AI Engineer Role AI Red Teaming RAG Portfolio Projects Search and RAG Project Checklist

An LLM and RAG production roadmap should start with a bounded user workflow, not with model selection. The model matters, but the product also needs retrieval, context packaging, and evaluation. Security, cost controls, monitoring, and a failure response path come next.

Paul Iusztin describes the full-stack AI engineer skill set in his AI engineering episode. At 22:29, the stack includes normal engineering work. At 29:12, RAG and knowledge management enter the build path. At 42:28, the topic becomes shipping AI products, not only building demos.

Use LLM Production Patterns for production design and AI Engineer Role for the role boundary. Use Search, RAG, and Knowledge Systems for retrieval architecture. LLM Evaluation Workflows covers the tests that keep a RAG system from becoming a demo with no regression path.

Own The Production Boundary

Treat production LLM work as software engineering plus model behavior management. The system must answer a real user task, explain where answers came from, and fail in observable ways. Cost, latency, privacy, and safety limits still apply.

Mariano Semelman gives the ownership version in From Notebook to Production: End-to-End AI Systems. At 17:27, the team owns the end-to-end system. At 37:39 and 41:28, business requirements and feedback loops remain part of the engineering path. At 55:28 and 1:02:53, notebooks give way to production services and observability tools. That makes this roadmap closer to Production and LLM Production Patterns than to a prompt-tuning checklist.

Start With A Small Assistant

Start with a narrow workflow by defining the user, task, input, and expected output. Name the failure modes, then build a simple prompt-based assistant with logs. Don’t add agents or vector databases until failure analysis shows why they’re needed.

Hugo Bowne-Anderson gives the evaluation-first loop in Practical LLM Engineering and RAG. At 13:56, he introduces generator-evaluator loops. At 23:00, gold tests make behavior measurable. At 26:43 and 27:38, failure analysis, logs, and traces show where to improve.

The first milestone should include:

a small set of representative test cases
expected outputs or grading criteria
logs and traces for each run
a failure analysis table
a decision about whether the problem needs retrieval

For a portfolio or capstone version, turn this milestone into a small project with RAG Portfolio Projects and the Search and RAG Project Checklist.

Add RAG For Changing Knowledge

RAG is useful when the answer depends on external, changing, or inspectable knowledge. Don’t describe it as model memory. It’s a retrieval and context-packaging system.

Meryem Arik separates RAG and fine-tuning in Deploying LLMs in Production. At 26:30, fine-tuning adapts model behavior. At 40:46, changing knowledge pushes the solution toward retrieval. At 42:02 and 46:42, grounding and retrieval patterns become production concerns.

Atita Arora gives the search version in Modern Search Systems. At 30:38, RAG combines search with generation. At 38:24 and 42:49, chunking and embeddings make the system inspectable. Prompt context and citations matter too. At 48:09, evaluation must cover retrieval and answer quality.

Use RAG vs Fine-Tuning when the failure could belong to retrieval, model behavior, or both.

Test Retrieval Before Generation

Debug RAG by separating retrieval failures from generation failures. Search may fail because documents are missing, chunks are weak, or ranking returns the wrong evidence. Generation may fail because prompt formatting is unclear or the model ignores context.

Daniel Svonava shows the search-engineering side in Building Production Search Systems. At 6:20 and 12:45, search quality depends on relevance, candidate generation, and ranking. At 16:45 and 30:22, chunking, ingestion, and embedding versioning affect later evaluations. At 34:00 and 52:35, hybrid search and vector database tradeoffs become system choices.

Production Search Evaluation, Vector Databases, and Search and RAG Project Checklist keep retrieval work testable.

Use Agents For Actions

Agent Engineering belongs later in the roadmap. Agents are useful when the system must plan, call tools, use memory, or take actions. They’re unnecessary when a search-backed answer is enough.

Ranjitha Kulkarni makes the boundary clear in Building Agentic AI Systems. At 11:00 and 12:31, agents combine tools, memory, and stores. At 36:11 and 37:39, retrieval can be a tool, but RAG and agents solve different problems. At 51:17, 53:20, and 56:02, agent evaluation needs custom evals and mocked tools. Outcome assertions matter too.

Aditya Gautam adds the enterprise version in The Future of AI Agents. At 30:26 and 36:55, guardrails, lineage, and feedback loops become operating requirements. At 43:30 and 50:18, multi-tenant evals and LLM judges make agent behavior measurable. Human labels matter too.

Harden Serving, Cost, and Security

Production work should make cost, latency, and security visible. At 16:48 and 18:46 in Deploying LLMs in Production, Meryem Arik compares open-source and API tradeoffs, then names provider drift as a production risk. At 49:44 and 51:35 in Deploying LLMs in Production, she moves from prototype APIs to production serving choices, including latency and cost.

Bartosz Mikulski gives the cost-control version in Production AI Engineering. At 28:16, prompt evaluation and cost are connected. At 30:00 and 31:45, prompt compression and caching reduce operating cost.

Maria Sukhareva gives the security version in Hardening Generative AI Chatbots. At 13:20, knowledge-base exfiltration is a real failure mode. At 16:15 and 17:00, output validation, query analysis, and non-LLM classifiers become part of a layered defense. Use AI Red Teaming for adversarial testing and LLM Production Patterns for monitoring controls.

Adjacent production-system topics: