In the rush to optimize large language model (LLM) workflows, developers frequently focus on reducing token counts to cut costs and speed up responses. Yet, as Siddhant K highlights in the introduction of Distill, this approach overlooks a critical underlying issue: the inherent unreliability of inputs from vector databases. When identical queries yield different retrieved chunks across runs—due to factors like database sharding or embedding drift—downstream LLM outputs become frustratingly inconsistent. You can't prompt your way out of bad input, as Siddhant emphasizes. Distill reframes the problem, targeting determinism rather than token economy to stabilize AI applications.

The Core Problem: Non-Determinism in RAG Pipelines

Retrieval-augmented generation (RAG) systems depend on vector databases like Pinecone to fetch relevant context chunks for LLMs. However, these retrievals often suffer from volatility—minor variations in input or database state can return dissimilar results for the same query. This inconsistency forces developers into endless tuning cycles, undermining trust in AI outputs. Siddhant argues that token-saving optimizations are superficial fixes; the real bottleneck is input reliability. Distill, an open-source tool written in Go, intercepts this issue post-retrieval but pre-inference, applying a multi-stage refinement process to enforce consistency.

How Distill Works: Clustering and Reranking for Stability

Distill's pipeline operates in four deliberate stages, all without invoking costly LLMs, ensuring efficiency:

  1. Over-fetching: Initially retrieves a broad set of 50 chunks from the vector database, casting a wide net to capture all potentially relevant context.
  2. Agglomerative clustering: Groups similar chunks using hierarchical clustering, identifying natural groupings based on semantic similarity. This step isolates redundant or overlapping information.
  3. Representative selection: Picks the most central or typical chunk from each cluster, preserving core ideas while eliminating noise.
  4. MMR reranking: Applies Maximal Marginal Relevance (MMR) to diversify the final selection, balancing relevance with novelty to cover multiple facets of the query.

The result is a distilled set of 8–12 high-quality chunks. Crucially, this process is deterministic—identical inputs always produce identical outputs—and adds only ~12ms of overhead. By running locally in Go, Distill avoids network latency and external API dependencies, making it ideal for latency-sensitive applications.

Implications for Developers and AI Infrastructure

Distill's approach has profound implications for AI engineering. First, it decouples reliability from database-specific quirks, currently supporting Pinecone with plans for Qdrant and Weaviate. Second, its minimal overhead (12ms) makes it viable for real-time systems, contrasting with LLM-based rerankers that incur significant cost and delay. Most importantly, determinism simplifies debugging and testing, allowing teams to build more robust RAG applications without unpredictable output variations.

As Siddhant invites discussion on algorithms and tradeoffs, Distill exemplifies a shift toward treating input quality as a first-class concern in AI pipelines. The tool is available on GitHub with a live playground for experimentation, encouraging community-driven refinement. For developers wrestling with erratic LLM behavior, Distill offers a pragmatic path to trustworthiness—proving that sometimes, the solution isn't smarter prompts, but smarter preprocessing.

Source: Siddhant K's announcement on Hacker News, with implementation details and access via GitHub and the Distill Playground.