Roadmap
LLM and RAG Production Roadmap
A podcast-backed roadmap for building LLM and RAG systems from bounded workflows to retrieval, evaluation, agents, security, cost, and monitoring.
Related Wiki Pages
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 the production reference and Search, RAG, and Knowledge Systems for retrieval architecture.
Common Definition
Across the archive, production LLM work is software engineering plus model behavior management. The system must answer a real user task. It must also 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.
Stage 1: Build The Smallest 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
Stage 2: Add RAG When Knowledge Changes
RAG is useful when the answer depends on external, changing, or inspectable knowledge. It shouldn’t be described 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.
Stage 3: Evaluate Retrieval Separately
Debug RAG by separating retrieval failures from generation failures. A bad answer may come from missing documents, poor chunking, or weak ranking. Prompt formatting or a model that ignores context can also cause it.
Daniel Svonava connects this to search engineering 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.
Use Production Search Evaluation, Vector Databases, and Search and RAG Project Checklist to keep retrieval work testable.
Stage 4: Add Agents Only For Action
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.
Stage 5: Harden Production
Production work should make cost, latency, and security visible. Meryem’s LLM deployment episode covers open-source versus API tradeoffs at 16:48 and provider drift at 18:46. At 49:44 and 51:35, prototypes and production serving become part of the architecture decision. Latency and cost matter there too.
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.
Related Pages
These pages cover the production systems around this roadmap: