What LlamaIndex/RAG Engineers Actually Build
RAG is about connecting AI to your data. Understanding what RAG engineers build helps you hire effectively:
Enterprise Knowledge Systems
Making company knowledge accessible:
- Internal knowledge bases - AI that searches company wikis, Confluence, Notion
- Policy assistants - Answer questions about company policies, procedures
- Onboarding bots - Help new employees find information
Examples: Many enterprise AI assistants use RAG architecture
Document Intelligence
Extracting value from documents:
- Contract analysis - Query legal documents with natural language
- Research synthesis - Summarize findings across papers and reports
- Technical documentation - AI-powered docs that answer questions
Examples: Legal tech, research tools, documentation platforms
Customer Support AI
Data-driven customer assistance:
- Product support - Answer questions using product docs
- FAQ automation - AI that draws from help center articles
- Ticket analysis - Find relevant past tickets for context
Examples: Many customer support AI tools use RAG
Domain-Specific AI Assistants
Specialized knowledge applications:
- Healthcare - Query medical literature and guidelines
- Finance - Search regulatory documents and reports
- Engineering - Technical specifications and manuals
Why RAG Matters
Understanding RAG's value helps you assess candidates:
The Problem RAG Solves
LLMs have a knowledge problem:
- Training cutoff - They don't know recent information
- Generic knowledge - They can't answer about YOUR data
- Hallucination risk - They confidently make things up
RAG fixes this by retrieving relevant context before generating responses.
How RAG Works (Simplified)
- Ingest - Process documents into searchable chunks
- Embed - Convert text to vectors for semantic search
- Store - Index vectors in a database (Pinecone, Weaviate, etc.)
- Retrieve - Find relevant chunks for each query
- Generate - Use LLM with retrieved context to answer
LlamaIndex vs. LangChain
Both frameworks enable RAG, but with different focuses:
- LlamaIndex - Specialized for data indexing and retrieval (RAG-first)
- LangChain - General-purpose LLM orchestration (broader scope)
Many developers use both together.
The RAG Engineer Profile
They Think About Data
Strong RAG engineers understand:
- Document processing - Parsing PDFs, HTML, docs of all types
- Chunking strategies - How to split documents effectively
- Metadata extraction - Capturing context beyond text content
- Data pipelines - Keeping indexes fresh and accurate
They Understand Search
RAG is fundamentally a search problem:
- Semantic search - Beyond keyword matching
- Hybrid search - Combining dense and sparse retrieval
- Re-ranking - Improving result ordering
- Query transformation - Reformulating queries for better retrieval
They Optimize Retrieval Quality
Getting good results is hard:
- Evaluation metrics - Measuring retrieval and answer quality
- Iterative improvement - Debugging and tuning retrieval
- Failure analysis - Understanding why queries fail
- Ground truth creation - Building evaluation datasets
Skills Assessment by Project Type
For Knowledge Base / Search Systems
- Priority: Chunking strategies, embedding selection, retrieval tuning
- Interview signal: "How would you handle documents with complex formatting like tables and images?"
- Red flag: Only knows basic similarity search
For Multi-Document Synthesis
- Priority: Cross-document retrieval, synthesis strategies, context compression
- Interview signal: "How do you answer questions that require information from multiple documents?"
- Red flag: Only single-document retrieval experience
For Production RAG Systems
- Priority: Pipeline reliability, evaluation frameworks, monitoring
- Interview signal: "How do you know when your RAG system is working well?"
- Red flag: No production experience, only prototypes
Common Hiring Mistakes
1. Confusing RAG with General LLM Work
RAG is a specialization. Building RAG systems requires:
- Document processing expertise
- Understanding of vector search
- Retrieval optimization skills
- Evaluation methodology
General "AI developer" may not have these specific skills.
2. Underestimating Data Quality Work
RAG quality depends heavily on data quality:
- Document cleaning and normalization
- Metadata extraction and enrichment
- Chunk boundary optimization
- Index maintenance and updates
This is often 70% of the work but overlooked in hiring.
3. Ignoring Evaluation Skills
RAG systems are hard to evaluate:
- How do you know retrieval is working?
- How do you measure answer quality?
- How do you detect regressions?
Ask about evaluation frameworks and strategies.
4. Over-Focusing on Framework Knowledge
LlamaIndex and tools change rapidly. Focus on:
- Understanding of RAG principles
- Search and retrieval fundamentals
- Problem-solving with data
- Ability to learn new tools
Recruiter's Cheat Sheet
Questions That Reveal Expertise
| Question | Junior Answer | Senior Answer |
|---|---|---|
| "How do you choose chunk size?" | "Use the default" | Discusses trade-offs: too small loses context, too large dilutes relevance. Mentions overlap, semantic boundaries, evaluation-based tuning |
| "How do you handle tables in documents?" | "Just extract the text" | Discusses structured extraction, table-aware chunking, using vision models for complex tables, metadata preservation |
| "How do you evaluate RAG quality?" | "Check if answers look right" | Explains retrieval metrics (MRR, NDCG), answer quality evaluation, LLM-as-judge, ground truth datasets, regression monitoring |
Resume Green Flags
- Production RAG systems in use
- Multiple document types processed
- Evaluation frameworks implemented
- Scale metrics mentioned (docs processed, queries handled)
- Experience with multiple vector databases
Resume Red Flags
- Only tutorial-level RAG projects
- No mention of evaluation or quality measurement
- Can't explain retrieval strategies
- Only worked with clean, simple documents
- No production deployment experience