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When Large Language Models (LLMs) generate plausible but incorrect answers—a phenomenon known as hallucination—or struggle with current events, developers turn to Retrieval-Augmented Generation (RAG) systems. Turtosa's engineering team recently chronicled their experience building Turtle, a RAG-powered chatbot that overcomes these limitations by grounding responses in verified data sources.

The RAG Architecture Breakdown

Turtle's system employs three core components:
1. Vector Database: ChromaDB stores document embeddings
2. Embedding Model: OpenAI's text-embedding-ada-002 converts text to vectors
3. LLM: GPT-3.5-turbo synthesizes responses using retrieved context

Documents undergo a meticulous ingestion pipeline: PDFs are parsed, split into chunks, converted to embeddings, then indexed in ChromaDB. During queries, relevant chunks are retrieved based on semantic similarity before being fed to the LLM.

Engineering Challenges and Solutions

  • Document Segmentation: Large PDFs required strategic chunking. The team implemented overlap between segments to preserve contextual continuity, preventing data fragmentation at section boundaries.
  • Context Window Constraints: With GPT-3.5-turbo's 16K token limit, the system prioritizes top-ranked chunks. This demanded careful relevance scoring to avoid omitting critical information.
  • Cost Optimization: Embedding generation proved expensive. The solution? Batch processing during ingestion and caching frequent queries.
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Why RAG Matters for Production AI

Unlike standalone LLMs, Turtle demonstrates:
- Accuracy: Responses cite source documents, reducing hallucinations
- Timeliness: Knowledge updates instantly when source documents change
- Transparency: Users receive references alongside answers

The team acknowledges ongoing challenges: balancing chunk size versus context retention, and handling complex multi-document queries. Future exploration includes fine-tuning open-source models to reduce dependency on paid APIs.

Source: Turtosa Blog