SkillHack › Career guides › Knowledge Engineer (RAG)
How to become an Knowledge Engineer (RAG)
Knowledge bases, embeddings, vector search, and graph RAG. Ground model answers in the right source of truth.
- 5phases in the roadmap
- 14topics to work through
- 17graded practice questions
- Freeno payment, ever
The Knowledge Engineer (RAG) roadmap
Each topic below carries a study note, real reading, and practice questions in the app. A topic unlocks when you pass the one before it, so the order is the path, so you are never guessing what to learn next.
Phase 1RAG retrieval foundations
What RAG is and when to use it
Retrieval-augmented generation grounds a model's answers in documents fetched at query time.
Embeddings and semantic search
Embeddings map text to vectors so semantically similar content lands close together, which is what lets retrieval find meaning rather than keywords.
Vector stores and indexes
A vector store holds embeddings and answers nearest-neighbour queries under a latency budget.
Phase 2Ingestion and indexing
Document parsing and extraction
Parsing quality caps retrieval quality: if extraction mangles a PDF's columns, tables, or scanned pages, no chunking or embedding can recover it.
Chunking strategies
How you split documents decides what retrieval can ever find.
Tables and structured content
Naive chunkers shred tables and structured layouts, scattering a row's meaning across fragments retrieval can no longer join.
Phase 3RAG retrieval quality
Hybrid search
Dense vectors capture meaning but are weak on exact and rare tokens - product codes, error strings, names.
Reranking and recall
Retrieval can return the right document ranked too low to use, or miss it entirely.
Query transformation
Real users ask vague, multi-part questions that retrieve poorly as one blurry vector.
Phase 4RAG grounding and citations
Grounding and citation requirements
RAG only earns trust when the model answers from the retrieved sources and cites them - and refuses when the sources do not support an answer.
Context budgeting
Retrieved passages compete for a finite context window with instructions, history, and the answer.
Phase 5RAG evaluation and production
Isolating retrieval vs generation failures
A wrong RAG answer has two possible causes: retrieval never surfaced the fact, or generation had it and ignored it.
Freshness and re-indexing
A knowledge base is only as good as it is current, and embeddings from different model versions are incompatible - so adopting a new embedder means re-embedding the whole corpus.
Production RAG: caching, cost, latency
In production, re-embedding, retrieval, reranking, and large contexts add up on every request, and many queries repeat.
Reading for this path
The primary sources behind the topics above, all free to read.
- Pinecone: retrieval-augmented generation
- Anthropic: contextual retrieval
- Pinecone: vector embeddings
- Pinecone: vector database
- LlamaIndex: loading data
- Pinecone: chunking strategies
- Pinecone: hybrid search
- Pinecone: rerankers
- LangChain: retrieval concepts
Coming from another job?
Most people on this path arrived from somewhere else: engineering, analysis, testing, product, support, compliance, design. Onboarding asks what you do today and builds a short starter run out of the gaps, so you begin from what you already know rather than from chapter one. See how it works.
Other AI career paths
LLMOps Engineer
Prompt and version management, evaluation gates, and cost and latency monitoring. Keep LLM applications reliable through every model and prompt change.
ML Engineer
Model selection, training and evaluation, experimentation, and statistical reasoning about the models you ship.
MLOps Engineer
Model CI/CD, registries, monitoring, and drift detection. Keep the path from training to production repeatable and observed.
Multimodal AI Engineer
Text, image, audio, and video together - fusion, cross-modal retrieval, and multimodal integration. Build AI that sees, hears, and reads at once.
NLP Engineer
Text pipelines, classification, extraction, and multilingual systems. Build the language layer of AI products.
Post-Training Engineer
Fine-tuning, RLHF, alignment tuning, and model specialization. Shape base models into products with the behavior you need.
Start the Knowledge Engineer (RAG) path for free
The full study notes, the reading, and the practice questions behind every topic above are in the app. Answer one tonight and you have started.
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