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Atita AroraandMike Jang
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* First draft - WIP * First draft - Added event details for Jan 17th and minor corrections * Version after review from Mike * Added blog section * Added Images (thanks Mike) * Update date so the article appears in the page with the list of blogs --------- Co-authored-by: Mike Jang <michael.jang@qdrant.io>
67 lines
3.6 KiB
Markdown
67 lines
3.6 KiB
Markdown
---
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title: "Navigating challenges and innovations in search technologies"
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draft: false
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slug: navigating-challenges-innovations
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short_description: Podcast on search and LLM with Datatalk.club
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description: Podcast on search and LLM with Datatalk.club
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preview_image: /blog/navigating-challenges-innovations/preview/preview.png
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date: 2024-01-12T15:39:53.751Z
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author: Atita Arora
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featured: false
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tags:
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- podcast
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- search
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- blog
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- retrieval-augmented generation
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- large language models
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---
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## Navigating challenges and innovations in search technologies
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We participated in a [podcast](#podcast-discussion-recap) on search technologies, specifically with retrieval-augmented generation (RAG) in language models.
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RAG is a cutting-edge approach in natural language processing (NLP). It uses information retrieval and language generation models. We describe how it can enhance what AI can do to understand, retrieve, and generate human-like text.
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### More about RAG
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Think of RAG as a system that finds relevant knowledge from a vast database. It takes your query, finds the best available information, and then provides an answer.
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RAG is the next step in NLP. It goes beyond the limits of traditional generation models by integrating retrieval mechanisms. With RAG, NLP can access external knowledge sources, databases, and documents. This ensures more accurate, contextually relevant, and informative output.
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With RAG, we can set up more precise language generation as well as better context understanding. RAG helps us incorporate real-world knowledge into AI-generated text. This can improve overall performance in tasks such as:
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- Answering questions
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- Creating summaries
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- Setting up conversations
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### The importance of evaluation for RAG and LLM
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Evaluation is crucial for any application leveraging LLMs. It promotes confidence in the quality of the application. It also supports implementation of feedback and improvement loops.
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### Unique challenges of evaluating RAG and LLM-based applications
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*Retrieval* is the key to Retrieval Augmented Generation, as it affects quality of the generated response.
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Potential problems include:
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- Setting up a defined or expected set of documents, which can be a significant challenge.
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- Measuring *subjectiveness*, which relates to how well the data fits or applies to a given domain or use case.
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### Podcast Discussion Recap
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In the podcast, we addressed the following:
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- **Model evaluation(LLM)** - Understanding the model at the domain-level for the given use case, supporting required context length and terminology/concept understanding.
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- **Ingestion pipeline evaluation** - Evaluating factors related to data ingestion and processing such as chunk strategies, chunk size, chunk overlap, and more.
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- **Retrieval evaluation** - Understanding factors such as average precision, [Distributed cumulative gain](https://en.wikipedia.org/wiki/Discounted_cumulative_gain) (DCG), as well as normalized DCG.
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- **Generation evaluation(E2E)** - Establishing guardrails. Evaulating prompts. Evaluating the number of chunks needed to set up the context for generation.
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### The recording
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Thanks to the [DataTalks.Club](https://datatalks.club) for organizing [this podcast](https://www.youtube.com/watch?v=_fbe1QyJ1PY).
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### Event Alert
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If you're interested in a similar discussion, watch for the recording from the [following event](https://www.eventbrite.co.uk/e/the-evolution-of-genai-exploring-practical-applications-tickets-778359172237?aff=oddtdtcreator), organized by [DeepRec.ai](https://deeprec.ai).
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### Further reading
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- https://qdrant.tech/blog
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- https://hub.superlinked.com/blog |