---
title: "Navigating challenges and innovations in search technologies"
draft: false
slug: navigating-challenges-innovations
short_description: Podcast on search and LLM with Datatalk.club
description: Podcast on search and LLM with Datatalk.club
preview_image: /blog/navigating-challenges-innovations/preview/preview.png
date: 2024-01-12T15:39:53.751Z
author: Atita Arora
featured: false
tags:
  - podcast
  - search
  - blog
  - retrieval-augmented generation
  - large language models
---

## Navigating challenges and innovations in search technologies

We participated in a [podcast](#podcast-discussion-recap) on search technologies, specifically with retrieval-augmented generation (RAG) in language models.

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.

### More about RAG

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.

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.

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:

- Answering questions
- Creating summaries
- Setting up conversations

### The importance of evaluation for RAG and LLM

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.

### Unique challenges of evaluating RAG and LLM-based applications

*Retrieval* is the key to Retrieval Augmented Generation, as it affects quality of the generated response.
Potential problems include:

- Setting up a defined or expected set of documents, which can be a significant challenge.
- Measuring *subjectiveness*, which relates to how well the data fits or applies to a given domain or use case.

### Podcast Discussion Recap

In the podcast, we addressed the following:

- **Model evaluation(LLM)** - Understanding the model at the domain-level for the given use case, supporting required context length and terminology/concept understanding.
- **Ingestion pipeline evaluation** - Evaluating factors related to data ingestion and processing such as chunk strategies, chunk size, chunk overlap, and more.
- **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.
- **Generation evaluation(E2E)** - Establishing guardrails. Evaulating prompts. Evaluating the number of chunks needed to set up the context for generation.

### The recording

Thanks to the [DataTalks.Club](https://datatalks.club) for organizing [this podcast](https://www.youtube.com/watch?v=_fbe1QyJ1PY).

### Event Alert
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).

### Further reading
- [Qdrant Blog](/blog/)
