Welcome back to this second lesson in the "Beyond Basic RAG: Improving our Pipeline" course! In the previous lesson, we explored ways to ensure that your language model stays grounded by responding only with information taken from retrieved context. That approach helps us avoid hallucinations and keeps the output reliable.

In this lesson, we'll improve the pipeline even further by making the retrieval process more iterative. Instead of collecting chunks of text just once before moving on to generation, we'll refine our queries step by step. This multi-stage retrieval can pinpoint the most relevant information and produce a more helpful final context.

Imagine a scenario where a user asks: "Tell me about the regulations for staff members." The question might be too broad. A typical retrieval step might find chunks containing some relevant information, but you might also want to narrow in on "internal policies" or "mandatory forms" for more precision.

Iterative retrieval does exactly that:

1. Retrieve an initial chunk based on the user's query.
2. Refine that query with a new keyword from the retrieved chunk (e.g., "internal" or "policies").
3. Repeat until you've gathered a set of chunks that thoroughly answers the question—or until improvements come to a point where the retrieval quality no longer improves meaningfully across iterations.

This multi-pass approach can drastically improve the depth and breadth of the retrieved information, making your final context more complete. Below, we'll walk through the building blocks of an iterative retrieval system in detail.

Imagine a user asks, "Tell me about the regulations for staff members." Our DB may include chunks like:

1. Chunk 1: "Our company requires that all staff members adhere to internal policies such as punctuality, dress code, and ethical behavior..."
2. Chunk 2: "Regulations for staff emphasize adherence to both internal policies and government standards, covering conduct, reporting, ..."

Iteration 1:

• Query: "Tell me about the regulations for staff members"
• Best match: Chunk 1 (score: 0.87)
• Extracted keyword: "internal"

Iteration 2:

• Updated Query: "Tell me about the regulations for staff members internal"
• Best match: Chunk 2 (score: 0.93)

Since further refinement doesn't significantly improve the score, the process stops. The system then uses these accumulated chunks to generate a grounded and comprehensive answer.

The first step is to define a function that fetches the best matching chunk given a query. Here’s how you can do this in Java, using a vector database collection:

What’s happening here?

1. We query our collection for the user’s queryText.
2. If no chunks are returned, we return null.
3. Otherwise, we pick the top chunk and calculate a simple inverted distance as a similarity score: 1 / (1 + bestDistance).
4. We return the chunk text and its metadata for further use.

Once you’ve retrieved a chunk, you often want to refine your query before the next retrieval pass. Here’s how you can extract a refinement keyword and append it to your query in Java:

Why is this useful?

• By filtering out stopwords, you reduce noise.
• By skipping words already in the current query, you avoid repetition.
• By capturing only longer words, you often get more meaningful terms.
• refineQuery simply appends the chosen keyword, shaping a more specific query for the next retrieval cycle.

Here’s how you can loop through multiple retrieval steps, each time refining the query and checking for improvement:

Notes on what's happening:

1. We limit to a certain number of steps (e.g., 3 iterations).
2. In each iteration, we retrieve the best matching chunk.
3. We compare its score to the best score so far. If there’s no improvement beyond a small threshold, we stop.
4. If we continue, we extract a fresh keyword from the chunk and add it to our query.
5. By the end, accumulatedChunks holds the chunks from each iteration.

While the iterative retrieval process we've outlined is powerful, it's important to recognize some practical considerations and limitations:

• Heuristics in Use: The choice of the longest word as a refinement keyword and the threshold for improvement are simple heuristics. These can be adjusted and improved based on the specific needs of your application.
• Complexity vs. Performance: More iterations can lead to better context but also increase computational cost. Balancing these is crucial for real-time applications.
• Simplifications: The current approach assumes that a single keyword can significantly refine a query, which might not always be the case. More sophisticated natural language processing techniques could be employed for better refinement.
• Limitations: The current method relies on the quality of the initial retrieval. If the first chunk is not relevant, subsequent iterations may not improve the context significantly.

You have now seen how iterative retrieval can make your RAG pipeline more robust by gradually honing in on the most relevant information. This lesson builds on our previous work about keeping generations grounded—and now you have an even better way to gather the right context in the first place.

Coming up next, you will get hands-on practice implementing and tweaking iterative retrieval strategies. Don't hesitate to experiment with different thresholds, numbers of steps, or query-refinement approaches. Each small tweak can make a big difference in the final performance of your RAG system.

Stay curious, and keep refining! You're making significant strides toward building a complete, high-performing retrieval-augmented generation pipeline.