Beyond Basic RAG: Enhancing RAG Systems with Constrained Generation in JavaScript

Introduction

Welcome to the first lesson of the " Beyond Basic RAG: Improving our Pipeline " course, part of the " Foundations of RAG Systems " course path! In previous courses, you delved into the basics of Retrieval-Augmented Generation (RAG), exploring text representation with a focus on embeddings and vector databases. In this course, we'll embark on an exciting journey to enhance our RAG systems with advanced techniques. Our focus in this initial lesson is on constrained generation, a powerful method to ensure that language model responses remain anchored in the retrieved context, avoiding speculation or unrelated content. Get ready to elevate your RAG skills and build more reliable systems!

Theoretical Foundations of Constrained Generation

When employing large language models (LLMs) in real-world applications, accuracy and fidelity to a trusted dataset are paramount. Even advanced LLMs can produce incorrect or fabricated information — often termed “hallucinations.” This is where constrained generation becomes indispensable. In essence, it is a form of advanced prompt engineering : we carefully craft instructions so the LLM only responds using the retrieved information or provides disclaimers when insufficient data is found. By shaping the prompt and enforcing rule-based fallback mechanisms, we instruct the LLM to: Use only the data you supply (the “retrieved context”). Provide disclaimers or refusal messages when context is insufficient. Optionally cite which part of the content it used. The result is a system less prone to made-up facts and more consistent with the original knowledge source.

Why Constrained Generation Is Important

LLM hallucination can be quite misleading. Imagine a scenario where your application confidently presents policies or regulations not present in your knowledge base. This can create confusion or even compliance issues. With constrained generation : The model remains grounded in the retrieved context only. Uncertain or unavailable information triggers a fallback message like “No sufficient data.” You can require the model to cite lines to verify the source of the answer, building trust with users.

Defining the Constrained Generation Function

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Demonstration of Retrieval and Constrained Generation

Below is a typical scenario. We assume documents have been chunked and stored in a vector database. We only show the retrieval steps briefly:

JavaScript

// Example demonstration of retrieval and constrained generation

// 1. Load and chunk a corpus
const chunkedDocs = loadAndChunkCorpus("data/corpus.json");

// 2. Build a collection in a vector database
const collection = await buildChromaCollection(chunkedDocs, "corpus_collection");

// 3. Run a sample query
const query = "Highlight the main policies that apply to employees.";
const retrievalResults = await collection.query({ queryTexts: [query], nResults: 2 });

// 4. Construct the retrieved context from top matches
let retrievedContext = "";
if (retrievalResults.documents[0]) {
    // Join the relevant chunks into one convenient string
    retrievedContext = retrievalResults.documents[0].map(docText => `- ${docText}`).join("\n");
}

// 5. Execute constrained generation function based on chosen strategy
const strategy = "strict";
const [answer, usedContext] = generateWithConstraints(query, retrievedContext, strategy);

// Example demonstration of retrieval and constrained generation

// 1. Load and chunk a corpus
const chunkedDocs = loadAndChunkCorpus("data/corpus.json");

// 2. Build a collection in a vector database
const collection = await buildChromaCollection(chunkedDocs, "corpus_collection");

// 3. Run a sample query
const query = "Highlight the main policies that apply to employees.";
const retrievalResults = await collection.query({ queryTexts: [query], nResults: 2 });

// 4. Construct the retrieved context from top matches
let retrievedContext = "";
if (retrievalResults.documents[0]) {
    // Join the relevant chunks into one convenient string
    retrievedContext = retrievalResults.documents[0].map(docText => `- ${docText}`).join("\n");
}

// 5. Execute constrained generation function based on chosen strategy
const strategy = "strict";
const [answer, usedContext] = generateWithConstraints(query, retrievedContext, strategy);

Under the hood:

We load the corpus, build a vector collection, and issue a query.
The top two documents are retrieved, combined, and passed to the constrained generation function.

Introduction

Theoretical Foundations of Constrained Generation

Why Constrained Generation Is Important

Defining the Constrained Generation Function

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Generating the Final Response

Demonstration of Retrieval and Constrained Generation

Practical Example: A Policy FAQ Bot

Conclusion and Next Steps