Table of Contents
Week 1: Setup & Discovery
What should happen:
- ✦ Understand data sources and document corpus
- ✦ Set up development environment (Python, vector DB, LLM APIs)
- ✦ Review existing prompts or RAG pipelines
- ✦ Identify quick wins and pain points
- ✦ Deliver: documentation of current state and recommendations
Week 2: First RAG Pipeline
What should happen:
- ✦ Build end-to-end RAG pipeline on sample data
- ✦ Implement chunking, embedding, retrieval, and generation
- ✦ Create basic evaluation harness
- ✦ Deliver: working RAG prototype on development set
Expected Deliverables by Day 30
A senior engineer should typically produce:
- ✦ Architecture diagram of the RAG system
- ✦ Documented evaluation metrics and baseline scores
- ✦ Prompt versioning strategy
- ✦ Cost estimation report for production usage
- ✦ Deployment plan with rollback procedures
Week 3: Evaluation & Optimization
What should happen:
- ✦ Build comprehensive evaluation framework
- ✦ A/B test chunking strategies, embedding models, retrieval methods
- ✦ Optimize prompts based on evaluation results
- ✦ Deliver: evaluation results and recommended configuration
Week 4: Production Readiness
What should happen:
- ✦ Deploy RAG pipeline to staging environment
- ✦ Set up monitoring (latency, cost, hallucination rate)
- ✦ Create runbooks for common issues
- ✦ Deliver: production-ready RAG system with monitoring
Warning Signs
Red flags:
- ✦ Week 2: Still struggling with basic RAG concepts
- ✦ Week 3: No evaluation framework, guessing at improvements
- ✦ Week 4: No deployment, still in Jupyter notebooks
- ✦ Can't explain retrieval metrics or hallucination detection
Tools Commonly Used
- ✦ LangGraph or workflow orchestration frameworks
- ✦ Vector databases such as Pinecone, Qdrant, Weaviate, or FAISS
- ✦ LLM providers such as OpenAI, Anthropic, or open-source models
- ✦ Evaluation frameworks such as Ragas or DeepEval
- ✦ Monitoring tools for latency, cost, and quality tracking
Set Clear Expectations
Senior LLM engineers ship production systems. They don't just run notebooks. Set these milestones on day one. Offline Pixel pre-vets senior LLM engineers who meet these expectations.
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