Introduction
Retrieval‑augmented generation (RAG) has become a cornerstone of modern conversational AI, allowing language models to pull in up‑to‑date facts from external documents while still leveraging their generative capabilities. Yet most tutorials stop at a single‑layer retrieval pipeline: a user question is encoded, a vector index is queried, the top passages are fed to a language model, and the answer is returned. In practice, many applications demand a more nuanced approach. They need to decide which knowledge source is most appropriate, verify the quality of the answer before presenting it, and refine the response if it falls short of the user’s expectations. This post walks through the construction of an agentic decision‑tree RAG system that addresses these challenges. By combining intelligent query routing, a self‑checking module, and an iterative refinement loop, the system behaves more like a human assistant that consults the right experts, double‑checks its work, and improves on the first attempt.
The architecture we build is intentionally modular. Each component can be swapped out for a different model or index without breaking the overall flow. We rely on open‑source tools such as FAISS for dense vector search, SentenceTransformers for embedding generation, and a lightweight LLM wrapper for the generative step. The decision tree is encoded as a set of rules that map query characteristics—such as keyword density, question type, or detected domain—to a particular knowledge source. The self‑checking module is a lightweight classifier that scores the answer’s confidence and flags potential hallucinations. Finally, the refinement loop re‑encodes the question together with the previous answer and re‑queries the index, allowing the system to iterate until a satisfactory confidence threshold is reached.
By the end of this tutorial you will have a working prototype that demonstrates how to build a robust, agentic RAG pipeline capable of handling diverse user intents, maintaining answer quality, and continuously improving its responses.
Main Content
Designing the Decision Tree
The first step is to formalize the decision logic that determines which knowledge base to consult. In many real‑world scenarios, a single monolithic index is insufficient. For instance, a customer support bot might need to consult a product FAQ, a policy document, and a technical manual. Rather than hard‑coding a single retrieval path, we construct a decision tree where each node evaluates a feature of the incoming query.
We extract features such as the presence of domain‑specific terminology, the presence of interrogative words, or the length of the query. These features are fed into a lightweight rule engine that outputs a source label. Each label corresponds to a pre‑built FAISS index containing embeddings from a particular corpus. The rule engine can be implemented as a simple Python function or as a more sophisticated decision‑tree library if the feature space grows.
The benefit of this approach is twofold. First, it reduces noise in the retrieval step by ensuring that only the most relevant documents are considered. Second, it provides an explicit audit trail: the system can log which rule fired and why, which is invaluable for debugging and compliance.
Intelligent Query Routing
Once the decision tree has selected a source, the system must route the query to the appropriate FAISS index. FAISS excels at fast approximate nearest‑neighbor search, but it requires that the query and documents share the same embedding space. Therefore, we use SentenceTransformers to encode both the query and the documents in the same dimensionality.
The routing layer performs a two‑stage search. In the first stage, it retrieves a coarse set of candidate passages from the chosen index. In the second stage, it re‑ranks these candidates using a cross‑encoder that scores the relevance of each passage to the query. This re‑ranking step is computationally heavier but only applied to a small subset, keeping latency low.
The output of the routing layer is a ranked list of passages that are then concatenated into a prompt for the generative model. By limiting the prompt to the most relevant passages, we reduce the risk of hallucination and improve answer accuracy.
Self‑Checking Mechanism
After the language model generates an answer, the system must evaluate its quality before delivering it to the user. We implement a self‑checking module that operates in two stages. First, a confidence estimator predicts the likelihood that the answer is correct. This estimator can be a simple logistic regression trained on a dataset of labeled answers, or a more advanced model that ingests the answer and the source passages.
Second, a hallucination detector scans the answer for unsupported claims. It does this by cross‑referencing the answer against the retrieved passages using a similarity metric. If the answer contains a claim that is not supported by any passage, the detector flags it. The flags are then combined with the confidence score to produce an overall quality metric.
If the quality metric falls below a predefined threshold, the system triggers the refinement loop. Otherwise, the answer is returned to the user.
Iterative Refinement Loop
The refinement loop is where the agentic nature of the system truly shines. When the self‑checking module flags a low‑confidence answer, the system re‑engages the retrieval and generation stages with additional context. One strategy is to append the previous answer to the query and re‑encode it, effectively asking the model to “explain” or “justify” its earlier response. This prompts the model to consult the knowledge base again, potentially surfacing new passages that were missed in the first pass.
We limit the number of refinement iterations to prevent infinite loops. After a maximum of three iterations, if the answer still does not meet the quality threshold, the system falls back to a safe response such as “I’m sorry, I don’t have enough information to answer that.” This fallback mechanism ensures that the system never delivers a low‑quality answer to the user.
Implementation with FAISS and SentenceTransformers
The practical implementation follows a straightforward pipeline. First, we pre‑process each corpus and generate embeddings using a SentenceTransformer model such as all-MiniLM-L6-v2. The embeddings are stored in a FAISS index, one per knowledge source. The decision tree rules are encoded in a Python dictionary that maps query patterns to index names.
During runtime, the system receives a user query, extracts features, and consults the decision tree to pick an index. The query is encoded, the FAISS index is queried, and the top passages are re‑ranked. The top passages are then concatenated with a system prompt and fed to a generative model such as GPT‑Neo or an open‑source LLM wrapped in the transformers library.
The self‑checking module is implemented as a small neural network that takes the generated answer and the top passages as input and outputs a confidence score. The hallucination detector uses a cosine similarity threshold to flag unsupported claims.
The entire pipeline is orchestrated using an async framework like FastAPI, allowing the system to handle multiple concurrent requests with low latency. Logging at each stage provides transparency and aids in monitoring the system’s performance.
Conclusion
Building an agentic decision‑tree RAG system elevates a simple retrieval‑generation pipeline into a robust, self‑aware assistant. By routing queries intelligently, verifying answer quality, and iteratively refining responses, the system mimics the human workflow of consulting experts, double‑checking facts, and revising conclusions. The modular design ensures that each component can be upgraded independently, whether it’s swapping in a newer embedding model, adding a new knowledge source, or improving the self‑checking classifier. As conversational AI continues to permeate customer support, education, and knowledge management, such agentic architectures will become essential for delivering reliable, high‑quality information.
Call to Action
If you’re excited to experiment with an agentic RAG pipeline, start by cloning the open‑source repository we’ve shared on GitHub. The repo contains scripts for building FAISS indices, defining the decision tree, and running the full inference loop. We encourage you to extend the decision tree with your own domain rules, fine‑tune the self‑checking model on domain‑specific data, and benchmark the system against standard QA datasets. By contributing back to the community—whether through pull requests, issue reports, or blog posts—you help accelerate the adoption of responsible, high‑quality AI assistants. Happy building!