Generative AI for LangChain Mastery Workshop Program

This hands-on, developer-focused workshop teaches how to build real-world LLM apps using LangChain and vector stores

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What is the LangChain Mastery Workshop Program?

The LangChain Mastery Workshop is a hands-on program that teaches developers to build real-world GenAI applications using LangChain, vector databases, and external tools—enabling end-to-end LLM workflows through structured learning and practical mini-projects.

Why Do Enterprises Need a LangChain Mastery Workshop Program?

As GenAI moves from hype to implementation, the LangChain Mastery Workshop Program helps enterprises to upskill their teams and tools to build secure, scalable, and impactful solutions.

Equip teams with hands-on experience to turn GenAI experiments into real applications using LangChain.
Train developers on essential components like LangChain, vector databases, and Retrieval-Augmented Generation (RAG) pipelines.
Build custom, secure GenAI solutions tailored to your business using internal data—avoid over-reliance on public AI tools.
Empower engineering, product, and R&D teams to rapidly prototype AI-powered solutions for real-world use cases.
Unlock the value of unstructured data by enabling contextual LLM responses and smarter interactions.
Unlock the value of unstructured data by enabling contextual LLM responses and smarter interactions.

Code, Connect, and Create with LangChain & Vector DBs

Supercharge your development skills by learning to apply LLMs for code generation, optimization, debugging, and unit testing in real-world scenarios.

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Prerequisites for the LangChain Mastery Workshop Program

To make the most of this workshop, participants should have a working familiarity with software development principles and a basic understanding of Generative AI concepts and tools

No deep AI/ML background is required, but comfort with writing code and working with APIs is expected.

What will you learn?

In this hands-on LangChain Mastery Workshop Program, you’ll learn how to:

By the end, you’ll have the skills and confidence to design, prototype, and deploy GenAI-powered applications.

Who Should Enroll?

This workshop is ideal for:

If you’re involved in building, integrating, or scaling AI-driven applications—this program is tailored for you.

Detailed Course Curriculum Outline

“Your First Step into the World of Generative AI.”

Module 1: LangChain Essentials

Chain types, agents, and tools
Memory and retrieval augmented generation

Module 2: Code Refactoring & Optimization with GenAI

Using FAISS, Weaviate, Qdrant
Chunking, embedding, and retrieval

Module 3: Connecting to APIs and External Tools

Plugging into search engines, docs, APIs

Module 4: Building a Multi-Step LLM Application

Design and implement a multi-turn GenAI app
Use case walkthrough. E.g. Support bot

Module 5: Hands-on Mini Project

Individual or group work on building a custom GenAI workflow

Learn to Build Real GenAI Workflows!

Master LangChain, vector databases, and multi-step LLM apps with API integration. Complete with a hands-on mini project to apply your skills.

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Duration & Mode

Duration

42 Hours (customizable for corporate needs)

Mode

Live Online / In-Person / Hybrid (as per corporate preference)

Hands-on Learning

Hands-on, Project based learning

Why Choose Benzne Consulting?

Master the Full LangChain Stack

From chains and agents to vector databases and memory management, we deliver a

complete hands-on guide to building advanced LLM-powered apps with LangChain.

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Use-Case Driven & Practical

Our course has an enterprise-focused, hands-on approach—combining practical implementation

where teams build multi-step GenAI workflows (like AI support bots) and connect them with real APIs, tools, and data sources.

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Deep Focus on RAG & Vector Databases

Learn how to use FAISS, Weaviate, and Qdrant effectively for retrieval-augmented generation,

embedding techniques, and document chunking for precise, high-quality results.

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API & Tool Integration Mastery

Your developers will learn to plug LangChain into web search, internal documentation, databases,

and APIs—enabling true enterprise-ready GenAI systems.

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Mini Project for Real-World Application

Our workshop also includes a mini-project for each participant or team where they build and

demo a custom LangChain solution—solidifying knowledge with contextual, hands-on experience.

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Custom-Tailored for Enterprise Use

We adapt the workshop to your team's skill level, tech environment, and target use cases

to maximize immediate business value.

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Our Trusted Clients

Learn to connect LLMs with external tools, use vector databases, and design full-stack GenAI workflows. End with a mini project that brings it all together!

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Instructor & Mentors of the program

Join the GenAI Builder’s Track – Apply Now

Our workshop will show you how to build real GenAI apps—not just experiments. Learn LangChain, vector databases, and deploy intelligent LLM-based systems at scale.

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Frequently Asked Questions About Addressing the LangChain Mastery Workshop Program

1. What will I learn in the LangChain course for GenAI and Vector DBs Application Development?

This workshop teaches participants how to bbuild end-to-end GenAI applications using LangChain, focusing on chaining logic, memory, tool use, and integration with vector databases like FAISS, Chroma, or Pinecone. You’ll learn how to build context-aware GenAI apps, perform document retrieval with embeddings, and design agentic workflows. The course covers use cases like Retrieval-Augmented Generation (RAG), conversational agents, and tools like OpenAI APIs and Hugging Face models. By the end, the participants will gain practical experience in chaining prompts, calling APIs, evaluating results, and deploying production-ready AI systems with robust architecture and observability features.

2. Who should take this course such as developers, backend engineers, or AI architects?

This course is ideal for backend developers, AI engineers, ML practitioners, solution architects, and technical product managers who want to build or integrate GenAI-powered applications. It’s particularly useful for teams working on LLM-enhanced systems, chatbots, document intelligence, or API-based agents. If your role involves designing, deploying, or maintaining AI-driven services, this course gives you the skills to move from experimentation to real-world applications using modern frameworks like LangChain.

3. Do I need prior experience with LangChain or GenAI to attend?

No prior experience with LangChain is required, but participants should have intermediate Python skills and some exposure to REST APIs or AI services. The course starts with the basics of how LangChain works and incrementally builds up to complex use cases. Even if you’ve only used GenAI tools like ChatGPT before, the course will guide you through how to programmatically interact with LLMs, chain them with tools and memory, and deploy applications that solve real problems.

4. Will the course cover hands-on implementation of LLM applications?

Yes. The course is heavily hands-on, with guided labs, notebook walkthroughs, and coding challenges. You’ll build real applications—such as document Q&A bots, API agents, and retrieval-based assistants—step-by-step. Each module reinforces theoretical concepts with implementation tasks so that by the end of the course, you’ll have working LangChain pipelines that you can extend, deploy, or integrate into production environments.

5. What types of chains, agents, and memory modules are covered in LangChain?

The course explores Sequential and Router chains, conversational memory (buffer and summary types), tool-using agents like AgentExecutor, and patterns such as ReAct for reasoning. The course explains how to build modular pipelines with prompt templates, manage multi-step interactions, and construct agents that call APIs or use calculators based on task goals. You’ll also understand when and how to use memory in LLM apps for personalization and multi-turn conversations.

6. How are FAISS, Weaviate, or Qdrant used in GenAI vector-based search?

These vector DBs are used to store and retrieve text embeddings for semantic search. The course shows how to chunk and embed documents using OpenAI, Hugging Face, or Cohere models, and then store them in FAISS, Weaviate, chroma or Qdrant. You’ll build retriever pipelines in LangChain that query these DBs and provide relevant context to LLMs—powering RAG workflows, document Q&A, and personalized assistants. The course includes demos for both local and hosted vector DBs. The choice of the DB and model can be zeroed down to align with organization’s preferences.

7. Does the course teach retrieval augmented generation (RAG) techniques?

Yes. RAG is a core focus. You’ll learn how to fetch relevant documents from vector stores, inject them into prompts, and reduce hallucinations in LLM outputs. The course covers prompt design for context injection, document chunking strategies, retrieval tuning, and evaluation methods. You’ll implement a complete RAG pipeline and understand its role in applications like enterprise search, chatbot grounding, and AI copilots that interact with proprietary data sources.

8. Will I learn how to connect GenAI apps to APIs and external tools?

Yes. You’ll build agents that call external APIs based on user inputs, or search engines, and connect with services like Notion, Google Sheets, or CRMs. LangChain’s tool interface allows you to define actions that LLMs can invoke as part of their reasoning loop. This enables use cases like automated task execution, report generation, and workflow orchestration—all powered by LLM-based agents capable of taking real-world actions.

9. What is the business value of the LangChain course for GenAI and Vector DBs?

The knowledge base is specific to the organization and is dynamic in nature. One can not keep on training and creating newer model with evolving data. Making this knowledgebase available to LLM engine for interpretation is the elementary use case of this course. This course enables teams to move from experimentation to real application development with GenAI. You’ll gain the skills to build internal tools like knowledge assistants, summarization engines, support bots, and intelligent search systems. It reduces dependency on manual workflows by empowering teams to create scalable, AI-integrated solutions. For businesses, this translates into faster decision-making, enhanced customer experiences, and significant cost and time savings in handling large volumes of unstructured data.

10. How does this training align with our GenAI app development goals?

If your organization is exploring GenAI for productivity, automation, or knowledge management, this course provides the technical foundation to support those initiatives. It trains your developers and architects to use LangChain, vector databases, and LLM APIs to build apps aligned with enterprise goals—whether it’s building chatbots, copilots, or data-driven assistants. The hands-on structure ensures skills are immediately applicable to internal POCs or MVPs, accelerating GenAI adoption within your ecosystem.

11. Can the course be customized for in-house use cases or proprietary tools?

Yes. The course can be tailored with your organization’s data sources, API endpoints, or infrastructure preferences. Whether you use proprietary tools, private LLMs, or custom data formats, the content can be adapted to reflect your environment. Custom modules can also be added to address domain-specific use cases in finance, legal, healthcare, or customer support—ensuring maximum ROI for your team’s learning efforts and faster deployment of GenAI initiatives.

12. What team roles will benefit most from this training program?

Backend developers, ML engineers, AI/ML architects, and DevOps professionals will benefit the most. Product managers and technical analysts exploring AI-enabled features will also gain practical insights. Teams working on digital transformation, knowledge automation, and intelligent systems will find the program particularly relevant. It’s designed to bridge AI theory with full-stack engineering practices—making it ideal for technical teams looking to ship GenAI apps, not just test them.

13. Does the course include a hands-on mini project or capstone demo?

Yes. The course concludes with a mini project where participants design and build an LLM-based application using LangChain and a vector DB. Sample projects include a document assistant, a conversational FAQ bot, or an agent-based tool that interfaces with APIs. This capstone consolidates everything learned—from chaining, retrieval, and memory to deployment. Participants can extend their mini project post-training into a POC for their own use cases or team demos.

14. Are post-course resources or certifications included?

Yes. Participants receive access to curated post-course materials—like code templates, notebooks, and recommended GitHub repos—for continued learning. Certifications are outside the scope of this program. However, the knowledge and skill gain may be useful in getting the certifications. Optional add-ons like mentorship hours or office-hour sessions may also be offered, depending on the delivery format. These resources help reinforce learning and provide a reference toolkit for building GenAI applications beyond the workshop timeline. Additionally, we undertake consulting assignments to support enterprises in customizing and deploying generative AI solutions tailored to their specific business needs—ensuring sustained impact beyond the training.

15. What practical outcomes can we expect from teams after completing the course?

After completing the course, teams will be able to prototype and deploy LLM-based applications, build RAG systems with LangChain, evaluate vector search performance, and integrate APIs for dynamic tool use. They’ll have the confidence and skills to lead GenAI development efforts internally—accelerating proof-of-concepts, improving data access, and enhancing product features with intelligence. Most importantly, teams will adopt a structured, maintainable approach to GenAI, reducing experimentation overhead and moving towards production readiness.