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.

Before we dive into the code, let's clarify what we mean by "iterative retrieval" and why it matters. This section will set the stage for the practical implementation details that follow.

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 level off.

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.

Now that you understand the concept, let's see how iterative retrieval works in practice. This example will help you visualize the process before we break it down into code.

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.

With this example in mind, let's move on to the code that enables each step of this process.

The first step in iterative retrieval is to fetch the most relevant chunk for a given query. In this section, we'll look at a function that does exactly that. We'll walk through the code and highlight the key parts with comments so you can see how the retrieval works under the hood.

This function takes a query, embeds it, and retrieves the best-matching chunk from your vector database. The comments above explain each step, from embedding the query to extracting the top result and its score.

Now that we can retrieve the best chunk, let's see how we can refine our queries to dig deeper.

After retrieving a chunk, the next step is to refine your query for the next iteration. This involves extracting a useful keyword from the retrieved chunk and appending it to your current query. The following code snippets show how to do this, with comments to clarify each part.

In these functions, we first extract a keyword from the retrieved chunk that isn't already in the query and isn't a common stopword. We then append this keyword to the query to make it more specific for the next retrieval step. The comments in the code walk you through the logic.

Also note that appending a keyword to the end of the query can sometimes create awkward or ambiguous phrases, which may reduce retrieval accuracy. When possible, consider rephrasing the query or inserting the keyword in a way that preserves the original meaning and grammar.

With the ability to retrieve and refine, let's see how to combine these steps into an iterative process.

Now that you know how to retrieve the best chunk and refine your query, let's look at how to loop through these steps to perform iterative retrieval. The following function demonstrates the full process, with comments to explain each stage.

This function repeatedly retrieves the best chunk, checks if the retrieval is improving, and refines the query with a new keyword. The process continues until the improvement threshold is not met or the maximum number of steps is reached. The comments in the code clarify each part of the loop.

Tip: What counts as a "significant improvement" depends on your dataset. In dense datasets, even a small score increase (e.g., 0.01) might matter, while sparser datasets may require a larger jump. Always calibrate improvement_threshold to fit your data.

With the iterative retrieval loop in place, let's discuss some practical considerations and limitations you should be aware of.

As you implement and experiment with iterative retrieval, it's important to keep a few practical points in mind. This section will help you anticipate challenges and make informed decisions as you refine your pipeline.

• 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.

By keeping these considerations in mind, you can better tailor the iterative retrieval process to your specific use case and constraints.

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.