Build Customizable LLM Pipelines with Haystack
Learn to design, implement, and optimize document search, Retrieval-Augmented Generation (RAG), question answering, and answer generation systems using Haystack’s modular AI framework.
What You’ll Learn
Haystack Foundations: Master the fundamentals of Haystack 2.0, its architecture, and how its components interact across real-world use cases.
Core Concepts: Gain hands-on experience with Pipelines, Components, Document Stores, and Retrievers.
Advanced Topics: Explore Hybrid Retrievers, Advanced Filtering, Rankers, and Self-Correcting Loops to improve retrieval precision and system robustness.
RAG Pipelines: Build and deploy Retrieval-Augmented Generation workflows using LLMs and Vector Stores, including techniques for guardrail implementation.
Prompt Engineering: Apply advanced prompting strategies such as Zero-Shot, Few-Shot, and Chain-of-Thought for more accurate, contextual results.
Requirements
This is an intermediate-to-advanced course designed for software engineers experienced in Python.
You should be comfortable with:
Git, Python, pipenv, and environment variables
Object-oriented programming, testing, and debugging
No prior experience in machine learning is required.
Course Description
Haystack is a powerful end-to-end framework for developing generative AI and NLP applications. Whether you’re building a document search engine, RAG pipeline, or intelligent Q&A system, Haystack simplifies the orchestration of embedding models and LLMs into scalable, production-ready pipelines.
In this comprehensive, project-driven course, you’ll progress from foundational understanding to hands-on mastery. Through guided exercises and real-world projects, you’ll learn how to construct, evaluate, and optimize LLM applications using Haystack 2.0 — from data ingestion to final answer generation.
Course Outline
Module 1: Haystack Foundations – Overview of Haystack 2.0 architecture and key components.
Module 2: Core Concepts – Deep dive into Pipelines, Document Stores, and Retrievers.
Module 3: Prompt Engineering – Learn and apply Zero-Shot, Few-Shot, and Chain-of-Thought prompting.
Module 4: Building a RAG Pipeline – Implement retrieval-augmented generation with vector databases and LLMs.
Module 5: Advanced Techniques – Enhance your pipelines with Rankers, Hybrid Retrieval, and self-correcting logic.
Module 6: Real-World Projects – Apply everything learned to create production-level AI solutions.
Who Should Enroll
AI Developers and Data Scientists building next-generation generative AI systems.
Software Engineers looking to deepen their applied AI and NLP engineering skills.
Technical Leaders seeking to understand and deploy AI-powered search and knowledge solutions.