The production RAG problem
Most RAG Systems Break Before They Ship
Enterprise RAG implementations that significantly underperform or fail within their first year — not because the idea was wrong, but because teams had no way to measure or diagnose retrieval quality.
RAG projects that never reach production. Retrieval quality issues, governance gaps, and the absence of an evaluation framework are the most cited reasons — not model capability.
Average base salary for RAG engineers in the US in 2026, ranging to $184K+ at the 90th percentile. RAG specialists are among the highest-demand roles in the current AI hiring cycle.
Projected RAG market growth from 2025 to 2035 — a nearly 20x expansion driven by enterprise adoption across legal, healthcare, finance, and developer tooling.
Learning outcomes
What You'll Be Able to Do
Build a complete naive RAG pipeline from scratch in TypeScript — document loading, chunking, embedding with OpenAI or Cohere, pgvector storage, and cosine similarity retrieval — and understand exactly where it will fail before you ship it
Diagnose retrieval failures by name: chunk boundary mismatches, low-precision dense retrieval, query-document vocabulary gaps — and apply the right fix from a ranked toolkit of hybrid search, BM25, reranking, HyDE, and multi-query expansion
Design a multi-strategy pipeline with routing logic, fallback paths, metadata filtering, and agentic tool-based retrieval — so your system degrades gracefully instead of silently returning bad context
Write a RAGAS evaluation harness that scores your pipeline on faithfulness, context precision, context recall, and answer relevancy — and run it automatically on every pipeline change so you know immediately when a 'fix' made things worse
Deploy with cost awareness: embedding caching, retrieval latency budgets, index maintenance strategies, and A/B testing infrastructure that lets you ship pipeline improvements with measured confidence
Hands-on from day one
What You'll Build
A Measurable Production RAG Pipeline
You don't read about RAG and then figure it out yourself. The course is backed by a real TypeScript project you clone locally. Starting from a bare pgvector database and a document corpus, you build a complete RAG pipeline incrementally across 4 modules. Each module adds a measurable improvement to the previous one — naive baseline, advanced retrieval, multi-strategy routing, evaluation harness. By the final module, you have a pipeline you can actually score, improve, and demo — not just one that 'seems to work' on your test queries.
- A naive RAG baseline: chunking strategy, OpenAI text-embedding-3-small, pgvector with cosine similarity, and a retrieval benchmark that scores it honestly
- Hybrid search combining dense vector retrieval with BM25 sparse search, fused with Reciprocal Rank Fusion — with before/after benchmark comparisons showing where it helps
- Query expansion pipeline: HyDE (Hypothetical Document Embeddings) and multi-query retrieval to bridge the gap between how users phrase questions and how documents are written
- A cross-encoder reranking layer that re-scores retrieved chunks against the query for precision — because top-k by cosine similarity is not the same as top-k by relevance
- A multi-strategy router with metadata filtering, fallback paths, and optional tool-based agentic retrieval for queries that don't fit the primary index
- A RAGAS evaluation harness with automated scoring on faithfulness, context precision, context recall, and answer relevancy — wired to run on every pipeline iteration
Before you start
Prerequisites
- —TypeScript or JavaScript experience — comfortable with types, generics, and async/await — the course won't hand-hold you on language basics
- —Some backend experience — you've worked with a database, you know what SQL looks like, and you understand the request/response model well enough to not be surprised by it
- —Familiarity with at least one LLM API — you've called OpenAI or Anthropic before, you know what a completion response looks like, and you've seen a tool call schema
- —Basic understanding of vector embeddings — you don't need to know the math, but you should know that embeddings are numeric representations of semantic meaning and that similarity search is how you find related content
21 lessons across 4 modules
Course Curriculum
Build naive RAG from scratch and understand it honestly. Document loading, chunking strategies and their failure modes, embedding model selection, pgvector setup, and cosine similarity search. Establish a benchmark before you touch anything advanced.
Go beyond similarity search. Hybrid search with BM25 and Reciprocal Rank Fusion, cross-encoder reranking, query transformation, Hypothetical Document Embeddings (HyDE), and multi-query retrieval — each technique applied to the baseline and measured against it.
Design systems that hold up under real usage. Query routing, multi-index retrieval, metadata filtering, fallback strategies, and agentic retrieval where the model decides what to fetch. When to use each pattern and how to compose them without turning your pipeline into a debugging nightmare.
Build the feedback loop that separates pipelines that ship from pipelines that stall. RAGAS evaluation metrics, automated quality scoring, A/B testing retrieval strategies, monitoring for retrieval drift, embedding and result caching, and cost optimization at scale.
Made for TypeScript AI engineers
Is This Course For You?
This is for you if…
- You've shipped at least one RAG integration and know it isn't working as reliably as you'd like — but you don't have the vocabulary or tooling to diagnose why
- You're building a knowledge-grounded AI feature and want to do it correctly from the start, not discover the failure modes after launch
- You've been through a naive RAG tutorial and understand the happy path — now you need the production path: evaluation, edge cases, and iterative improvement
- You're a TypeScript developer and want to stay in TypeScript — pgvector, embeddings, retrieval logic, and evaluation harness, all without touching Python
- You're responsible for the quality of an AI system and need a systematic way to measure it, not just rely on "it feels better" feedback from users
This is NOT for you if…
- You're still learning TypeScript or Node.js fundamentals — this course builds on backend experience and won't slow down for language basics
- You're looking for Python content — every line of code in this course is TypeScript against a pgvector database
- You want a conceptual overview without writing code — every lesson involves implementation, and the evaluation harness will tell you honestly whether your pipeline is improving
- You're looking for ML theory, fine-tuning, or model training — this is a software engineering course about retrieval systems, not machine learning research
Got questions?
Frequently Asked Questions
What's wrong with naive RAG?
Nothing in a demo. The problem surfaces at scale and in the long tail of real queries.
Naive RAG — chunk your documents, embed the chunks, store in a vector database, retrieve by cosine similarity, inject as context — works well when users phrase queries exactly the way your documents are written, when the answer fits neatly inside one or two chunks, and when you're testing on representative queries you already know the answers to.
In production: retrieval returns chunks that score high on semantic similarity but contain the wrong answer. Users ask "how do I cancel?" and your retrieval returns a chunk about refund policies because they're topically related but not what the user needs. The LLM then generates a confident-sounding answer grounded in the wrong context — and unless you have an evaluation framework, you'll never know.
The failure modes are: chunk boundary mismatches (the answer spans two chunks that are never retrieved together), query-document vocabulary gaps (users use different words than the docs), low-precision dense retrieval (top-5 by cosine similarity isn't the same as top-5 by usefulness), and the complete absence of any signal about whether your pipeline is degrading over time. This course covers all of them.
Do I need prior AI or machine learning experience?
No. This is a software engineering course about building retrieval systems, not a machine learning course. The math behind embeddings (high-dimensional vectors, cosine similarity, dot products) is explained in engineering intuition terms — you'll understand what's happening and why it matters without needing to work through the linear algebra. No statistics, no model training, no Python.
If you're comfortable with TypeScript, async/await, and have worked with a relational database, you have everything you need.
Why pgvector instead of Pinecone or Qdrant?
Three reasons.
First, pgvector runs in PostgreSQL — the database most TypeScript teams are already running. You add an extension, create a column with type vector(1536), and you have a vector store. No new managed service, no new billing relationship, no new operational complexity.
Second, pgvector supports hybrid queries: you can combine vector similarity search with SQL filters in a single query. Metadata filtering (retrieve only documents from this tenant, this date range, this category) is a WHERE clause, not a separate API.
Third, the retrieval patterns this course teaches — hybrid search, reranking, multi-index strategies — transfer directly to any vector store. Once you understand how retrieval works in pgvector, moving to Pinecone or Qdrant is a matter of swapping the client, not rethinking the architecture.
What is RAGAS and why does it matter?
RAGAS (Retrieval Augmented Generation Assessment) is an evaluation framework that gives you quantitative scores for your RAG pipeline without requiring a manually labeled test set.
It measures four things: faithfulness (is the answer actually grounded in what was retrieved, or is the model confabulating?), context precision (is the retrieved context relevant to the question?), context recall (did retrieval surface all the context needed to answer correctly?), and answer relevancy (does the generated answer actually address what was asked?).
The reason it matters: without a measurement framework, you can't improve systematically. You might try HyDE, feel like the answers are better, and ship it — only to discover three weeks later that recall dropped and you're now missing critical context for a different class of queries. RAGAS gives you a regression test suite for your retrieval pipeline. Module 4 builds the evaluation harness and wires it into the development workflow.
What is HyDE?
Hypothetical Document Embeddings is a query expansion technique that addresses one of the most common failure modes in semantic search: the vocabulary gap between how users ask questions and how documents are written.
Standard retrieval: embed the user's query, find chunks with similar embeddings.
The problem: "how do I get a refund?" has a different embedding than a policy document that says "customers are entitled to a full reimbursement within 30 days of purchase." They're semantically related but the surface-level wording diverges enough that cosine similarity underperforms.
HyDE: ask the LLM to generate a hypothetical answer to the query ("A customer can request a refund within 30 days..."), embed that hypothetical answer, then retrieve against it. The hypothetical answer is written in document-style prose, so it has much higher embedding similarity to your actual documents. You discard the hypothetical answer and use the retrieved real chunks as context.
It adds one LLM call per query. For knowledge-dense corpora where user language and document language diverge, the precision improvement is usually worth the cost. Module 2 covers HyDE alongside multi-query retrieval so you can measure both against your baseline.
How does this relate to the LangGraph.js course?
The two courses teach complementary skills at different layers of the stack.
This course is about retrieval: getting the right context into your model reliably, understanding why retrieval fails, and building a measurement framework so you know when your pipeline is working. The output is a scored, improvable retrieval pipeline.
The LangGraph.js course is about agent orchestration: building stateful multi-step workflows where agents maintain memory, call tools, route decisions, and coordinate with each other. The output is a production-grade agent architecture.
These meet in practice. A LangGraph.js agent that answers questions about your knowledge base needs a retrieval pipeline behind its tool calls. The RAG pipeline you build in this course can be wired directly into a LangGraph.js tool node. Many developers take both. There's no content overlap between the two courses — they pick up at different abstraction layers and neither duplicates the other.
What embedding models does the course use?
OpenAI text-embedding-3-small as the primary model throughout the course. It's cost-effective ($0.02 per million tokens), high quality for most retrieval tasks, and the correct default for TypeScript teams building their first production RAG system.
The course also covers Cohere embed-v3 as an alternative, particularly for its native float16 and int8 quantization support which reduces storage costs significantly at scale. Lessons include provider-switching notes so you can substitute your preferred embedding provider without rewriting retrieval logic.
Model selection is covered explicitly: when to upgrade to text-embedding-3-large, when Cohere's reranking models add value, and how to benchmark embedding model choices against your specific document corpus rather than generic leaderboard scores.
How long does the course take?
Approximately 45 hours at a comfortable pace — 21 lessons across 4 modules. Most developers complete one module per week while working full time. There's no time limit; access is lifetime with all future updates included.
Do I need API keys?
Yes. The course uses OpenAI for embeddings (text-embedding-3-small) and for the generation step (GPT-4o or equivalent). You can use Anthropic (Claude) for generation — lessons include provider-switching notes.
Typical API costs during the course are $10–$30 depending on how many chunking strategies and retrieval variants you experiment with. Module 1 establishes a benchmark corpus small enough that embedding costs are negligible; later modules scale up.
What's the sandbox project?
A TypeScript project backed by a local PostgreSQL database with the pgvector extension. Each lesson ships with skeleton files, typed interfaces, and failing tests. The evaluation harness from Module 4 is introduced in Module 1 as a benchmark framework — you're measuring your pipeline from the naive baseline forward, not retrofitting evaluation at the end.
You write the implementation; the test suite tells you whether retrieval quality improved or regressed.
When does the course launch?
The course is planned for Q4 2026. Join the waitlist to be notified on launch day. Waitlist members receive early access pricing when the course goes live.
Is there a money-back guarantee?
Yes. 30 days, no questions asked.