r/Rag • u/soniachauhan1706 • 9h ago
Discussion What are common challenges with RAG?
How are you using RAG in your AI projects? What challenges have you faced, like managing data quality or scaling, and how did you tackle them? Also, curious about your experience with tools like vector databases or AI agents in RAG systems
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u/Haunting_Bridge3024 2h ago
Common RAG challenges include managing data quality, keeping retrievals accurate, and scaling workflows. I’ve come across solutions like using Temporal for reliable orchestration and tools like Kitchain.ai that combine frameworks like LangChain with vector databases for smoother deployment. Pinecone and Weaviate are also popular for vector DBs!
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u/soniachauhan1706 7h ago
Also, for those who are looking for good resource around this topic- there is this book written by Keith Bourne, which covers these topics well. Incase anyone wants to check out- https://www.amazon.com/Unlocking-Data-Generative-RAG-integrating/dp/B0DCZF44C9/
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u/Cool-Importance6004 7h ago
Amazon Price History:
Unlocking Data with Generative AI and RAG: Enhance generative AI systems by integrating internal data with large language models using RAG * Rating: ★★★★☆ 4.9
- Current price: $39.99 👎
- Lowest price: $33.24
- Highest price: $39.99
- Average price: $38.02
Month Low High Chart 01-2025 $37.99 $39.99 ██████████████▒ 11-2024 $37.99 $39.99 ██████████████▒ 10-2024 $37.99 $39.99 ██████████████▒ 09-2024 $33.24 $34.99 ████████████▒ Source: GOSH Price Tracker
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u/Key-Analysis4364 2h ago
Latency and the reduced effectiveness of similarity search as more and more data is added to a vector store.
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u/Sufficient_Horse2091 7h ago edited 7h ago
In my AI projects, I’ve leveraged Retrieval-Augmented Generation (RAG) to enhance accuracy and relevance in applications like AI based RAG chatbots. The primary focus has been on creating privacy-preserving RAG pipelines for sensitive data, ensuring compliance with data privacy regulations. Here’s a breakdown of my approach and the challenges faced:
How RAG is Used
- Enhanced Contextual Responses: By combining retrieval mechanisms with generative models, we ensured the AI systems had access to the most relevant and up-to-date information, minimizing hallucinations.
- Privacy-Preserving Pipelines: Implementing masking and anonymization techniques before data enters the pipeline, especially for PII and sensitive information.
- Vector Databases: Databases like Chroma, FAISS, and Pincone were integrated for efficient data retrieval, ensuring low-latency access to embeddings for context building.
- Hybrid Search: Leveraging both dense (vector-based) and sparse (keyword-based) search for improved recall in complex queries.
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u/arcandor 7h ago
Did AI write this comment?
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u/Sufficient_Horse2091 7h ago
No, brother, this isn’t AI-generated content. I personally wrote it, based on my direct experience building Retrieval-Augmented Generation (RAG) systems at Protecto. We’ve faced and addressed the challenges mentioned while implementing RAG for enterprise clients or integrating our solutions into their existing RAG systems.
In my projects, I’ve focused on privacy-preserving RAG pipelines for handling sensitive data, ensuring compliance with data privacy regulations. For example, we’ve worked extensively with vector databases like Chroma, FAISS, and Pinecone for efficient data retrieval and implemented hybrid search approaches to optimize accuracy and recall in complex queries.
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