I’d like to understand how data is ingested and utilized in Azure AI Language services, specifically for the Conversational Language Understanding (CLU) and Conversational Question Answering (CQA) features.

kirti yadav 20 Reputation points
2025-05-29T13:37:12.8+00:00
  • Is the data embedded first before generating responses?
  • How is the data stored or accessed during runtime?
  • How does this approach differ from a traditional RAG (Retrieval-Augmented Generation) based system?

Looking forward to insights on the internal workflow and architectural differences.

Azure AI Language
Azure AI Language
An Azure service that provides natural language capabilities including sentiment analysis, entity extraction, and automated question answering.
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  1. Azar 29,515 Reputation points MVP Volunteer Moderator
    2025-05-29T22:09:14.18+00:00

    Hi there kirti yadav

    Thanks for using QandA platform

    From what I’ve seen, in Azure AI Language’s CLU, data is basically ingested during training – you define intents and examples, and the model learns from that. So at runtime, it doesn’t really look anything up; it just uses what it learned. CQA, on the other hand, builds a knowledge base from your documents, kind of like an index, and at runtime it searches that index and generates responses. It’s not quite like a RAG system where it dynamically retrieves and embeds documents during every query – the retrieval part in CQA is kind of “pre-baked” when you upload your data. RAG is more flexible in that it retrieves fresh data each time, but it can be slower. In CQA, your data’s static unless you update the KB. Hope this helps!

    If this helps kindly accept the answer thanks much.

    1 person found this answer helpful.
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  1. Ravada Shivaprasad 375 Reputation points Microsoft External Staff Moderator
    2025-05-29T22:20:31.8166667+00:00

    Hi kirti yadav

    In traditional large language models (LLMs), data is embedded during the pretraining phase, where the model learns statistical patterns from vast corpora and encodes this knowledge into its parameters. At runtime, the model does not access external data sources; instead, it generates responses based solely on its internalized knowledge. This means that once trained, the model cannot dynamically incorporate new or domain-specific information unless it is fine-tuned or retrained.

    In contrast, Retrieval-Augmented Generation (RAG) systems introduce a hybrid architecture where the model can access external knowledge bases during inference. In a RAG pipeline, data is first embedded into vector representations and stored in an index. When a user query is received, the system retrieves the most relevant documents from this index using similarity search, and these documents are then passed along with the query to the LLM to generate a response. This allows RAG systems to provide more accurate and up-to-date answers without retraining the model

    Reference : Evaluate RAG with LlamaIndex

    Architecturally, the key difference lies in how information is accessed: traditional LLMs rely entirely on static, pre-embedded knowledge, while RAG systems dynamically retrieve and incorporate external data at runtime. Advanced RAG frameworks like DRAGIN further enhance this by allowing the model to decide when and what to retrieve based on its internal information needs during generation, making the process more adaptive and context-aware.

    I Hope this helps. Do let me know if you have any further queries. If this answers your query, please do click Accept Answer and Yes for was this answer helpful.

    Thank you!

    1 person found this answer helpful.

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