• phone icon +44 7459 302492 email message icon support@uplatz.com
  • Register

BUY THIS COURSE (GBP 12 GBP 29)
4.8 (2 reviews)
( 10 Students )
 

Haystack

Master Haystack to design, build, and deploy scalable Retrieval-Augmented Generation (RAG), search, and question-answering systems using LLMs and vect
( add to cart )
ALL COURSES Course URL
Save 59% Offer ends on 31-Dec-2025
Course Duration: 10 Hours
  Price Match Guarantee   Full Lifetime Access     Access on any Device   Technical Support    Secure Checkout   Course Completion Certificate
Bestseller
Trending
Popular
Coming soon (2026)

Students also bought -

Completed the course? Request here for Certificate. ALL COURSES

As large language models (LLMs) become widely adopted across enterprises, one of the biggest challenges organizations face is grounding model responses in reliable, up-to-date, and private data. While LLMs excel at reasoning and text generation, they lack direct access to proprietary documents, internal knowledge bases, and dynamic data sources. This limitation has led to the rapid rise of Retrieval-Augmented Generation (RAG) architectures.
 
Haystack is an open-source AI orchestration framework designed specifically to build search systems, question-answering pipelines, and RAG-based LLM applications. It enables developers to combine document stores, retrievers, rankers, generators, and large language models into modular, production-ready pipelines. Haystack abstracts the complexity of building AI systems that integrate LLMs with real-world data sources.
 
Originally developed by deepset, Haystack has evolved into one of the most widely used frameworks for enterprise search, knowledge assistants, and LLM-powered applications. It supports modern LLMs (OpenAI, Azure OpenAI, Hugging Face, open-weight models), vector databases, traditional keyword search, hybrid retrieval, and advanced evaluation workflows.
 
Modern AI applications—such as internal knowledge assistants, document chat systems, legal research tools, customer support bots, and enterprise copilots—require more than just a single model call. They require pipelines that retrieve relevant data, rank results, generate grounded responses, and evaluate quality. Haystack provides exactly this capability through its flexible pipeline architecture.
 
The Haystack course by Uplatz offers a comprehensive, hands-on learning experience focused on building real-world LLM applications. Learners will understand how Haystack works internally, how to design robust RAG pipelines, how to integrate vector databases and LLM providers, and how to deploy Haystack applications into production environments.
 
This course emphasizes practical implementation, enterprise use cases, scalability, and observability. By the end of the course, learners will be able to design and deploy production-grade AI systems that combine retrieval, reasoning, and generation—safely and efficiently.
🔍 What Is Haystack?
 
Haystack is an open-source framework for building AI pipelines focused on search, question answering, and Retrieval-Augmented Generation.
 
Key capabilities include:

  • Building RAG pipelines for LLMs

  • Document ingestion and preprocessing

  • Dense, sparse, and hybrid retrieval

  • Integration with vector databases

  • LLM-based answer generation

  • Modular pipeline orchestration

  • Evaluation and benchmarking tools

  • Production-ready APIs

Haystack is framework-agnostic and works with a wide range of models and infrastructure.
⚙️ How Haystack Works
 
Haystack is built around a pipeline-based architecture, where each component performs a specific task.
 
1. Document Stores
 
Haystack supports multiple document stores, including:

  • Elasticsearch / OpenSearch

  • FAISS

  • Milvus

  • Weaviate

  • Pinecone

  • Chroma

These stores manage documents and embeddings used for retrieval.
2. Retrievers
 
Retrievers identify relevant documents based on a query. Haystack supports:

  • Dense retrievers (embeddings)

  • Sparse retrievers (BM25)

  • Hybrid retrievers

This flexibility ensures high recall across use cases.
3. Rankers
 
Rankers re-order retrieved documents to improve relevance before generation, improving answer quality.
4. Generators (LLMs)
 
Generators use LLMs to produce grounded answers based on retrieved context. Haystack integrates with:

  • OpenAI & Azure OpenAI

  • Hugging Face models

  • Open-weight LLMs (Llama, Mistral, Phi, etc.)

5. Pipelines & Orchestration
 
Haystack pipelines connect all components into a single workflow, enabling:

  • Question answering systems

  • Conversational chat with documents

  • Semantic search engines

  • Knowledge assistants

🏭 Where Haystack Is Used in Industry
 
Haystack is widely adopted for enterprise AI solutions.
 
1. Enterprise Knowledge Assistants
 
Internal document search and Q&A for employees.
 
2. Customer Support Automation
 
Ticket resolution and FAQ chatbots powered by company data.
 
3. Legal & Compliance Research
 
Search and analysis of legal documents and policies.
 
4. Healthcare & Life Sciences
 
Clinical document analysis and literature review.
 
5. Financial Services
 
Risk analysis, policy search, and regulatory Q&A.
 
6. Developer Tools & Copilots
 
Code and documentation assistants grounded in repositories.
 
Haystack excels in data-sensitive and enterprise environments.
🌟 Benefits of Learning Haystack
 
By mastering Haystack, learners gain:

  • Practical LLM application development skills

  • Deep understanding of RAG architectures

  • Experience with vector databases and retrieval

  • Ability to build enterprise-ready AI assistants

  • Knowledge of evaluation and optimization techniques

  • High-demand skills in applied AI and MLOps

Haystack expertise is critical for modern LLM engineers.
📘 What You’ll Learn in This Course
 
You will learn how to:

  • Understand Haystack architecture

  • Ingest and preprocess documents

  • Build dense, sparse, and hybrid retrievers

  • Integrate vector databases

  • Design RAG pipelines

  • Connect LLMs for answer generation

  • Evaluate retrieval and generation quality

  • Build chat-with-documents systems

  • Deploy Haystack APIs

  • Build production-grade AI assistants

🧠 How to Use This Course Effectively

  • Start with basic QA pipelines

  • Practice document ingestion and retrieval

  • Experiment with different retrievers

  • Add LLM-based generation

  • Build conversational RAG systems

  • Evaluate and optimize results

  • Complete the capstone project

👩‍💻 Who Should Take This Course
 
This course is ideal for:

  • LLM Engineers

  • NLP Engineers

  • Machine Learning Engineers

  • AI Product Developers

  • Data Scientists

  • Backend Engineers building AI systems

  • Students entering applied AI roles

🚀 Final Takeaway
 
Haystack enables organizations to build trustworthy, data-grounded LLM applications that go far beyond generic chatbots. By combining retrieval, ranking, and generation into modular pipelines, Haystack provides the foundation for scalable, enterprise-ready AI systems.
 
By completing this course, learners gain the skills required to design, deploy, and maintain modern RAG and search-based AI applications—skills that are essential in today’s LLM-driven landscape.

Course Objectives Back to Top

By the end of this course, learners will:

  • Understand Haystack internals

  • Build RAG pipelines with LLMs

  • Integrate vector databases

  • Design enterprise search systems

  • Evaluate and optimize AI pipelines

  • Deploy production-ready Haystack applications

Course Syllabus Back to Top

Course Syllabus

Module 1: Introduction to Haystack

  • LLM application challenges

  • Why RAG matters

Module 2: Haystack Architecture

  • Components and pipelines

Module 3: Document Ingestion

  • Preprocessing and indexing

Module 4: Retrieval Techniques

  • Dense, sparse, and hybrid retrieval

Module 5: Generators & LLMs

  • Integrating LLM providers

Module 6: RAG Pipelines

  • End-to-end pipeline design

Module 7: Conversational AI

  • Chat with documents

Module 8: Evaluation & Optimization

  • Metrics and benchmarking

Module 9: Deployment & Scaling

  • APIs and production setup

Module 10: Capstone Project

  • Build a full enterprise-ready RAG assistant

Certification Back to Top

Upon completion, learners receive a Uplatz Certificate in Haystack & RAG-Based LLM Applications, validating expertise in production-grade AI pipelines.

Career & Jobs Back to Top

This course prepares learners for roles such as:

  • LLM Engineer

  • NLP Engineer

  • AI Application Developer

  • Applied Machine Learning Engineer

  • Enterprise AI Engineer

Interview Questions Back to Top

  1. What is Haystack?
    An open-source framework for building RAG and QA systems.

  2. What problem does Haystack solve?
    Grounding LLM responses in real data.

  3. Which databases does Haystack support?
    Elasticsearch, FAISS, Pinecone, Weaviate, and more.

  4. What is RAG?
    Retrieval-Augmented Generation.

  5. Can Haystack work with any LLM?
    Yes, via integrations.

  6. Is Haystack open source?
    Yes.

  7. What are pipelines in Haystack?
    Composable AI workflows.

  8. Is Haystack enterprise-ready?
    Yes.

  9. Does Haystack support evaluation?
    Yes.

  10. Who should use Haystack?
    Teams building data-grounded LLM apps.

Course Quiz Back to Top
Start Quiz
Back to Top



BUY THIS COURSE (GBP 12 GBP 29)