--- title: [Hands-on] Audio RAG with 200x Cheaper Vector DB Costs description: A hands-on tutorial builds an audio RAG system: transcribing audio with Speechmatics, embedding transcripts with MongoDB's voyage-context-3 contextualized... canonical: https://daily.dev/posts/hands-on-audio-rag-with-200x-cheaper-vector-db-costs-zgitujzhk twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: [Hands-on] Audio RAG with 200x Cheaper Vector DB Costs | daily.dev og:description: A hands-on tutorial builds an audio RAG system: transcribing audio with Speechmatics, embedding transcripts with MongoDB's voyage-context-3 contextualized... og:url: https://daily.dev/posts/hands-on-audio-rag-with-200x-cheaper-vector-db-costs-zgitujzhk og:image: https://api.daily.dev/og/posts/zGitUJzHK.png og:image:alt: [Hands-on] Audio RAG with 200x Cheaper Vector DB Costs og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # [Hands-on] Audio RAG with 200x Cheaper Vector DB Costs **[Daily Dose of Data Science \| Avi Chawla \| Substack](https://daily.dev/sources/dailydoseofds)** · 7 min read · 0 upvotes · 0 comments ## Summary A hands-on tutorial builds an audio RAG system: transcribing audio with Speechmatics, embedding transcripts with MongoDB's voyage-context-3 contextualized chunk embedding model, storing vectors in MongoDB Atlas Vector Search, retrieving context, and generating answers with DeepSeek V3.2 via OpenRouter, orchestrated with LlamaIndex and wrapped in a Streamlit UI. The newsletter also covers Opik's approach to surfacing unreported agent bugs through aggregate trace queries, and walks through eight prompting techniques (few-shot, CoT, prompt hierarchy, role-specific, negative, JSON, ARQ, verbalized sampling) with benchmark figures for each. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How do I build a RAG system over audio data? Transcribe audio using Speechmatics, then embed the transcript with MongoDB's voyage-context-3 contextualized chunk embedding model and store it in MongoDB Atlas Vector Search. At query time, convert the text query into an embedding, run vector search to retrieve relevant context, then generate a response with DeepSeek V3.2 served via OpenRouter, using LlamaIndex for orchestration and a Streamlit UI for the interface. _daily.dev surfaces practical RAG architecture walkthroughs for engineers wiring up audio and vector search pipelines._ ### What makes voyage-context-3 different from other chunk embedding models? Voyage-context-3 is a contextualized chunk embedding model that produces chunk embeddings with full document context, unlike common chunk embedding models that embed chunks independently. This means each chunk's embedding reflects the surrounding document rather than being computed in isolation, which can improve retrieval accuracy in RAG pipelines. _Engineers picking an embedding model for retrieval quality can track comparisons like this on daily.dev._ ### How much does attentive reasoning queries (ARQ) improve instruction adherence over chain-of-thought and direct prompting? In testing across 87 scenarios within the Parlant framework, ARQ achieved a 90.2% success rate compared to 86.1% for chain-of-thought prompting and 81.5% for direct prompting. ARQ replaces free-form CoT with targeted, domain-specific questions in a predefined JSON schema, reinstating critical constraints at the point where reasoning happens using the recency effect. _daily.dev helps developers tuning LLM prompting strategies keep up with techniques like ARQ._ ## Similar posts on daily.dev - [Building a RAG Application with Spring Boot, Spring AI, MongoDB Atlas Vector Search, and OpenAI](https://daily.dev/posts/building-a-rag-application-with-spring-boot-spring-ai-mongodb-atlas-vector-search-and-openai-vhyy5w1do) · InfoQ · 2 upvotes · 0 comments - [RAG Explained Simply with a Real Project](https://daily.dev/posts/rag-explained-simply-with-a-real-project-pgy15jzg7) · freeCodeCamp · 23 upvotes · 1 comments - [RAG vs. Jev \+ RAG, clearly explained\!](https://daily.dev/posts/rag-vs-jev-rag-clearly-explained--u6qyp2kg7) · Daily Dose of Data Science \| Avi Chawla \| Substack · 29 upvotes · 0 comments - [Chat with Your Documents: A Practical Guide to RAG Using the New Laravel AI SDK](https://daily.dev/posts/chat-with-your-documents-a-practical-guide-to-rag-using-the-new-laravel-ai-sdk-xrytwkwo0) · Tighten · 0 upvotes · 0 comments --- Tags: [#prompt-engineering](https://daily.dev/tags/prompt-engineering), [#rag](https://daily.dev/tags/rag), [#vector-search](https://daily.dev/tags/vector-search), [#embeddings](https://daily.dev/tags/embeddings), [#llama](https://daily.dev/tags/llama) [View this post on daily.dev](https://daily.dev/posts/hands-on-audio-rag-with-200x-cheaper-vector-db-costs-zgitujzhk) ```json 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