Haystack - Customizable LLM pipelines for GenAI Applications

Haystack – Customizable LLM pipelines for GenAI Applications | Udemy [Update 06/2024]
English | Size: 1.03 GB
Genre: eLearning

Learn techniques for document search,RAG,content generation,Evaluation, Data Science techniques with Haystack components

What you’ll learn
Learn fundamentals of Haystack 2.0. Haystack theory with hands on examples
Haystack 2.0 Concepts: Pipelines, Components, Document Store, Retrievers, Evaluators
Haystack 2.0 Advance Topics : Re-Ranker, Hybrid Retriever, Advanced Filtering, Self Correcting Loops, Agentic Pipeline using OpenAI’s function calling
Prompt Engineering : Learn prompt engineering techniques – Zero Shot, Few Shot and Chain of Thoughts
Model Based Evaluators – Faithfulness Evaluator(LLM-As-A-Judge), SAS Evaluator, Context Relevance Evaluator
Advanced Retrieval Techniques : Filter Retriever, Sparse Keyword Based Retriever, Dense Embedding Retriever and Sparse Embedding Retriever – SPLADE
RAG Pipeline: Learn about RAG, Vector store

Haystack is an end-to-end framework that accompanies you in every step of the GenAI project life cycle. Whether you want to perform document search, retrieval-augmented generation (RAG), question answering, or answer generation, Haystack can orchestrate state-of-the-art embedding models and LLMs into pipelines to build end-to-end NLP applications and solve your use case.

This beginner to advanced all-encompassing course aims to swiftly show you how to leverage the Haystack 2.0 library for LLM applications. You will acquire the expertise and insights required to create state-of-the-art LLM solutions across a wide array of subjects.

What you’ll learn:

Haystack Foundations : Understand fundamentals of Haystack2.0 by learning Haystack components. Haystack theory with hands on example
Real-World Applications : Implement Haystack components with real world applications
Prompt Engineering : Learn prompt engineering techniques – Zero Shot, Few Shot and Chain of Thought
RAG Pipeline : Learn about RAG, Vector store
Haystack 2.0 Concepts : Pipelines, Components, Document Store, Retrievers, Evaluators
Advanced Retrieval Techniques : Filter Retriever, Sparse Keyword Based Retriever, Dense Embedding Retriever and Sparse Embedding Retriever – SPLADE(Sparse Lexical and Expansion Model)
Model Based Evaluators – Faithfulness Evaluator(LLM-As-A-Judge), SAS Evaluator, Context Relevance Evaluator
Haystack 2.0 Advance Topics : Re-Ranker, Hybrid Retriever, Advanced Filtering, Self Correcting Loops, Conversation/Agentic Pipeline using OpenAI’s function calling, LLM-As-A-Judge, Model Based Evaluation

During the course, you will engage in practical exercises and real-world projects to solidify your grasp of the concepts and methods discussed. By the course’s conclusion, you will be skilled in utilizing Haystack to develop robust, efficient, and adaptable LLM applications for a broad range of uses.

Who Should Enroll:

AI developers, data scientists, business leaders looking to acquire skills in building generative AI-based applications with Haystack