Iterative Retrieval

Iterative Retrieval is an AI skill for implementing multi-step information retrieval workflows that progressively refine search results through query reformulation, relevance filtering, and context accumulation. It covers query expansion, re-ranking strategies, feedback loops, chunk aggregation, and retrieval evaluation that enable more accurate document retrieval for RAG systems.

What Is This?

Overview

Iterative Retrieval provides structured approaches to improving search result quality through multiple retrieval passes. It handles reformulating initial queries based on partial results to capture missed relevant documents, re-ranking retrieved chunks using cross-encoder models for improved ordering, accumulating context across iterations to build comprehensive answer grounding, filtering irrelevant results through relevance scoring thresholds, expanding queries with synonyms and related terms to broaden recall, and evaluating retrieval quality through precision and recall metrics.

Who Should Use This

This skill serves AI engineers building RAG pipelines that need high retrieval accuracy, search engineers optimizing result quality for knowledge bases, developers implementing research assistants that gather information iteratively, and teams building question-answering systems over large document collections.

Why Use It?

Problems It Solves

Single-pass retrieval misses relevant documents when the initial query does not match document terminology. Top-k results from embedding similarity may rank tangentially related chunks above directly relevant ones. Complex questions requiring information from multiple sources cannot be answered from a single retrieval pass. Without relevance filtering, irrelevant chunks dilute the context provided to the generation model.

Core Highlights

Query reformulation adapts search terms based on initial results to capture missed documents. Re-ranking with cross-encoders improves result ordering beyond embedding similarity. Context accumulation gathers information across iterations for multi-faceted answers. Relevance thresholds filter low-quality matches to maintain context precision.

How to Use It?

Basic Usage

from dataclasses import dataclass

@dataclass
class Chunk:
    text: str
    score: float
    source: str

class SimpleRetriever:
    def __init__(self, chunks):
        self.chunks = chunks

    def search(self, query, top_k=5):
        scored = []
        query_terms = set(query.lower().split())
        for chunk in self.chunks:
            words = set(chunk.text.lower().split())
            overlap = len(query_terms & words)
            score = overlap / max(len(query_terms), 1)
            scored.append(Chunk(
                text=chunk.text, score=score,
                source=chunk.source
            ))
        scored.sort(key=lambda c: c.score, reverse=True)
        return scored[:top_k]

    def iterative_search(self, query, rounds=3, top_k=5):
        all_results = []
        seen = set()
        current_query = query
        for _ in range(rounds):
            results = self.search(current_query, top_k)
            for r in results:
                if r.text not in seen and r.score > 0.1:
                    all_results.append(r)
                    seen.add(r.text)
            if results:
                top_text = results[0].text
                new_terms = set(top_text.lower().split()[:5])
                current_query = query + " " + " ".join(new_terms)
        return all_results

Real-World Examples

class IterativeRAGRetriever:
    def __init__(self, vector_store, reranker=None):
        self.store = vector_store
        self.reranker = reranker

    def retrieve(self, query, max_rounds=3, threshold=0.3):
        context = []
        seen_ids = set()
        current_query = query

        for round_num in range(max_rounds):
            results = self.store.similarity_search(
                current_query, k=10
            )
            if self.reranker:
                results = self.reranker.rerank(
                    current_query, results
                )
            new_chunks = []
            for r in results:
                if r.id not in seen_ids and r.score >= threshold:
                    new_chunks.append(r)
                    seen_ids.add(r.id)
            context.extend(new_chunks)
            if not new_chunks:
                break
            current_query = self.reformulate(
                query, context
            )
        return context

    def reformulate(self, original, context):
        keywords = set()
        for chunk in context[-3:]:
            words = chunk.text.lower().split()
            keywords.update(words[:5])
        expansion = " ".join(list(keywords)[:10])
        return f"{original} {expansion}"

    def evaluate(self, retrieved, relevant_ids):
        retrieved_ids = {r.id for r in retrieved}
        relevant = set(relevant_ids)
        tp = len(retrieved_ids & relevant)
        precision = tp / max(len(retrieved_ids), 1)
        recall = tp / max(len(relevant), 1)
        return {
            "precision": round(precision, 3),
            "recall": round(recall, 3)
        }

Advanced Tips

Use cross-encoder re-ranking after initial embedding retrieval to improve precision on the top results. Limit iteration rounds with a convergence check that stops when no new relevant chunks are found. Weight later iterations lower when combining scores to prefer early high-confidence matches.

When to Use It?

Use Cases

Use Iterative Retrieval when building RAG systems that need high recall for complex questions, when single-pass retrieval misses relevant documents due to vocabulary mismatch, when answering multi-faceted questions that require information from several sources, or when improving search quality in knowledge base applications.

Related Topics

Vector database querying, cross-encoder re-ranking, query expansion techniques, RAG pipeline architecture, and retrieval evaluation metrics complement iterative retrieval.

Important Notes

Requirements

Vector store or search engine supporting similarity queries. Optional re-ranking model for improved result ordering. Evaluation dataset with relevance labels for quality measurement.

Usage Recommendations

Do: set relevance score thresholds to filter low-quality matches from accumulated context. Limit iteration rounds to prevent diminishing returns and excessive latency. Evaluate retrieval quality with precision and recall metrics before tuning parameters.

Don't: run unlimited iterations without convergence checks, which adds latency without quality improvement. Skip re-ranking when precision matters more than recall. Accumulate all retrieved chunks without deduplication, which wastes context window tokens.

Limitations

Each iteration adds latency proportional to the search and re-ranking time. Query reformulation quality depends on the relevance of initial results. Iterative approaches increase API costs when using hosted embedding and re-ranking services.