LangChain and LLM Application Development Corporate Training
Turn LLM prototypes into your team's production advantage with hands-on, instructor-led training.
Give your enterprise teams the LLM application skills that turn LangChain into working products. Edstellar's LangChain & LLM Application Development corporate training equips your engineers with practical techniques to build chains, wire up RAG pipelines, and deploy agents safely. Delivered live by experienced trainers, onsite or virtually, each session is tailored to your stack, roles, and goals, with measurable outcomes you can report to leadership.
Instructor-led group trainingVirtual / on-site / off-site10 languagesDelivered inEnglish, Spanish, Mandarin, German, Arabic, Portuguese, Hindi, French, Japanese, Italian100+ countriesWe have delivered inUnited States, United Kingdom, India, Canada, Germany, France, Netherlands, UAE, Saudi Arabia, Singapore, Australia, Japan, Brazil, South Africa and 90+ more countries
How we build a LangChain-ready team
THE EDSTELLAR LLM APPLICATION CAPABILITY MODEL
Assess LLM app baseline
Skill checks reveal where your team stands with LLMs, prompts, and LangChain today.
One LangChain standard
Set shared patterns for prompts, chains, RAG, and agent design across your team.
Practice in hands-on labs
Build chains, RAG pipelines, and agents in guided labs with instructor coaching.
Prove the lift
Re-run skill checks and app evaluations so leadership sees capability rise.
Scale across teams
Roll out one LLM application standard across teams, batches, and locations.
Need more than one program?Get a group quote
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Program Overview: What is LangChain and LLM Application Development Corporate Training?
LangChain & LLM Application Development is a hands-on technical program for engineering teams that build AI-powered applications. Your developers and data practitioners learn LangChain fundamentals, prompt and chain design, memory handling, retrieval-augmented generation, and agent tool integration. The program also covers testing, tracing, and safe deployment, so teams ship large language model applications with confidence.
Every engagement begins with a training needs analysis, so the curriculum maps to your team's real LLM use cases and stack rather than a generic syllabus. Instructors lead interactive sessions with guided labs where learners build chains, wire up vector stores, and plug tools into agents step by step. Each lab includes instructor feedback and debugging support, plus peer code reviews of prompts and chains. The program closes with a capstone: a production-grade LLM application your team designs, tests, and deploys.
Teams prototype LLM features quickly, but without shared patterns chains break in production, RAG answers hallucinate, and tool-using agents misbehave. This program replaces trial-and-error prompting with disciplined design: prompt templates, evaluation habits, and deployment guardrails. Your team returns with a repeatable way to move LLM ideas from demo to dependable feature.
The capstone is a real, deployable LLM application built from your team's own documents or workflows. Learners assemble the full stack: a RAG pipeline or tool-using agent, traced and evaluated, with tests around prompts, retrieval quality, and cost. They leave with runnable code, reusable prompt and chain templates, a testing and tracing playbook, and a deployment checklist they can apply to the next LLM project on their roadmap.
Teams prototype LLM features fast, but without shared patterns, chains break in production, RAG answers hallucinate, and agents fail on edge cases. This program builds one clear standard: design prompts and chains deliberately, ground answers in your data, and test every LLM application before it reaches users.
The result: engineers who ship reliable LLM applications, from RAG pipelines to tool-using agents, supporting faster feature delivery and safer AI use across your organization.
What's Included in this Workshop for Your Enterprise Teams
- 1
LangChain foundations
Core LLM app ideas your team uses daily.
- 2
Hands-on LangChain labs
Build chains, RAG, and agents live with coaching.
- 3
Deployment toolkits
Reusable patterns for LLM app rollout.
- 4
Expert trainers
Learn from practitioners in applied LLM apps.
- 5
Capstone LLM project
Ship a working LLM app for a real use case.
- Training delivered at any of your offices globally
- 10+ languages
- Schedule around your calendar
- Group size set along with you
- Run as one cohort or split into batches
- Instructor-led (virtual/onsite/hybrid)
- Recurring and annual options
- Trainer sourcing, matching and logistics
Build your team's skills. Anywhere in the world.
Real-world use cases
from day one
Instructor-led training
for your teams
Delivered globally
in 100+ countries
Enterprise Training Solutions Customized for Your Company
One partner for instructor-led corporate training, delivered the way your teams work.
Request a Training Quote- Customized Training Delivery
- Scale Your Training: Small to Large Teams
- In-person Onsite, Live Virtual or Hybrid Training Modes
- Plan from 2000+ Industry-ready Training Programs
- Hands-On Labs on Live Infrastructure, Led by Industry Experts
LangChain and LLM Application Development Course Topics and Program Outline
A six-module technical program on building LLM applications with LangChain: foundations, chains and memory, RAG pipelines, agents and tools, testing and deployment. Five guided hands-on labs plus a capstone app build.
Built by Edstellar with LangChain trainers
Built by practitioners in LLM applications, with lab feedback on every exercise your team completes.
Baseline each participant's LangChain readiness before module 01 begins
Build strong LLM application foundations in instructor-led sessions for your team
Reinforce skills through guided hands-on labs on LangChain chains, RAG, and agents
Apply LangChain skills to real-world scenarios and a capstone application build
A short pre-training assessment sets each participant's entry level, so the program starts where your team actually is. The rest is weighted toward hands-on practice: guided LangChain labs on chains, RAG pipelines, and agents and realistic scenarios drawn from your team's work, not slides.
Delivered as
Entry level set by the assessment. Depth and schedule customized to your team.
a)How LLM applications work
b)LangChain architecture and core concepts
c)Environment setup and first chain
d)Baseline skills check
a)Prompt templates and output parsers
b)Designing sequential chains
c)Memory types for conversation state
d)Chain lab: build a working flow
a)Document loaders and chunking
b)Embeddings and vector stores
c)Retrievers and grounded answers
d)RAG lab: question answering app
a)Agent types and planning loops
b)Connecting tools and APIs
c)Handling failures and retries
d)Agent lab: multi-step assistant
a)Evaluating chains and agents
b)Tracing runs and debugging issues
c)Guardrails for safe responses
d)Deployment lab: package the app
a)Integrate skills across modules
b)Build a production-ready LLM app
c)Present the app for review
d)Leave with a deployment plan
Customize this program tailored to your team?
Tell us your training needs: what your team already works with, the level they’re at, and the topics you want them to focus on. We’ll adjust the modules, duration and hands-on labs, and send you a customized outline for your group.
Key Skills and Outcomes Your Team Will Gain
By the end of this program, your engineers will build, evaluate, and deploy LLM applications with LangChain, from prompt chains and RAG pipelines to tool-using agents and production rollout.
Build LLM chains in LangChain
Assemble prompt templates, chains, and memory into working LLM application flows.
Design RAG pipelines
Load, chunk, and embed documents, then retrieve grounded context for answers.
Create LLM agents with tools
Connect LLMs to tools and APIs so agents plan, act, and complete tasks.
Evaluate LLM outputs reliably
Test chains and agents for accuracy, hallucination, and consistency before release.
Add guardrails to LLM apps
Apply validation, moderation, and error handling to keep LLM apps safe.
Trace and debug chains
Use tracing and logging to diagnose issues and track app behavior.
Deploy LLM applications
Package and deploy LangChain apps for team and enterprise use.
Teams that Benefit from this LangChain and LLM Application Development Group Training
This program is built for engineers and technical teams who build AI features: AI engineers, software developers, data engineers, and product teams. Content adapts to role and experience, so every cohort practices on the LLM applications and workflows they actually own.
Ship working LLM apps faster.
AI engineering teams learn to build reliable LangChain applications, from prompt chains to tool-using agents. They practice design, evaluation, and deployment.
Workflow friction
- Prompt chains break in production
- RAG answers hallucinate facts
- Agents fail on edge cases
How the program helps
- Build reliable LLM chains
- Ground answers with RAG
- Ship agents that recover
Practice use cases
- Chain labs
- RAG drills
- Agent builds
Add LLM features with confidence.
Software teams learn to integrate LLMs cleanly into products with shared LangChain patterns, strong testing, and fast debugging.
Workflow friction
- LLM APIs behave unpredictably
- No shared LangChain patterns
- Debugging chains is slow
How the program helps
- Integrate LLMs cleanly
- Reuse proven chain patterns
- Trace and debug faster
Practice use cases
- API labs
- Tool builds
- Debug sessions
Power RAG with solid data flows.
Data teams learn to build ingestion and indexing pipelines that keep retrieval quality high, plus evaluation harnesses for LLM apps.
Workflow friction
- Document ingestion is messy
- Vector indexes drift and bloat
- No evaluation harness in place
How the program helps
- Build robust RAG data flows
- Keep retrieval quality high
- Measure app quality clearly
Practice use cases
- Loader labs
- Index tuning
- Eval drills
Turn LLM ideas into real features.
Product and platform teams learn to prototype with LangChain, balance cost and latency, and ship LLM features with guardrails.
Workflow friction
- Prototypes never reach users
- Unclear LLM cost and latency
- Safety reviews slow launches
How the program helps
- Prototype with LangChain fast
- Balance cost and latency
- Ship with guardrails
Practice use cases
- Prototype labs
- Prompt tuning
- Launch reviews
Each cohort works through role-relevant LangChain labs, then leaves with reusable patterns for day-to-day work.
Prerequisites: This program benefits network engineers, RF designers, planners, and wireless researchers most. Familiarity with basic wireless communications concepts helps, but is not required: foundations are taught from the ground up. No prior radar or 6G experience is needed, and labs adapt to each cohort's tools, whether teams work in simulation or on lab hardware.
From prototype tinkerers to production LLM app developers
LangChain Foundations and Chains, Prompts & Memory introduce the framework's building blocks and memory design. Practice modules build production muscle: RAG with Vector Stores grounds answers in enterprise data, Agents & Tool Integration wires LLMs to real tools, and Testing, Tracing & Deployment hardens quality. The capstone ships a Production LLM App built end to end.
Developers progress from understanding LangChain components to building, testing, and deploying production-grade LLM applications.
Mixed cohort, or several teams at once?
We can tailor the training to different teams, roles, and experience levels within the same program.
Certificate of Training Completion.
On successful completion, employees receive an Edstellar course completion certificate, recognizing their commitment to ongoing learning and professional development. It validates the skills they have built and motivates them to keep growing and contribute to organizational success.
Earned, not attended. Awarded on completion of all hands-on labs and the capstone project.
Training Delivery Modes: Onsite, Virtual and Hybrid
Every mode is instructor-led and tailored to your LLM stack before delivery. The curriculum, hands-on labs, assessments, and capstone stay the same across onsite, live virtual, and hybrid formats, so capability is consistent however you run it.
Onsite Training
Your instructor delivers at your facility with labs set up for your team. Every engineer builds chains, RAG pipelines, and agents with coaching. Best for co-located teams wanting maximum interaction.
- Instructor delivers at your facility
- Labs built around your LLM use cases
- Build LLM apps together with coaching
- Best for co-located teams wanting maximum interaction
Live Virtual Training
Live virtual sessions run instructor-led on your preferred platform. Your team joins LangChain labs, breakout exercises, and real-time code reviews with the instructor. Ideal for distributed teams needing hands-on practice without travel.
- Live instructor-led sessions on your platform
- Guided labs with breakout troubleshooting
- Screen-shared code reviews in real time
- Ideal for distributed teams across locations
Hybrid Training
Live instruction plus supervised hands-on labs, across onsite and virtual sessions. Your team builds chains, RAG pipelines, and agents with instructor coaching. Best for engineering teams with flexible schedules and real build work.
- Live instruction across onsite and virtual sessions
- Guided LangChain labs with instructor coaching on your roles
- Build and test LLM apps in real time
- Best for flexible schedules with real hands-on practice
Need a training format that works for your team?
Full days, half days across weeks, or split by module. Tell us the delivery window and we schedule the cohort around it.
Frequently Asked Questions
Share team size, preferred dates, and delivery mode in the lead form, or book a short discovery call first. We reply with a tailored proposal covering scope, schedule, and pricing for your group.
Yes. Book a short discovery call and we will map your training needs, LLM stack, and timelines before sharing a plan.
Yes. Live virtual sessions run instructor-led on your preferred platform, with hands-on LangChain labs, breakout exercises, and real-time feedback.
Yes. We deliver onsite at your offices, with the agenda and labs adapted to your team's stack, use cases, and workflows.
The standard program runs over two full days. Duration can be adjusted to fit your schedule, from focused one-day sessions to extended multi-day formats.
Question not answered here?
Put it in the request. A training specialist answers it directly, within one business day.
Where this Training Fits in Your Team's learning path.
LangChain work follows general AI engineering in Python and leads into retrieval and agents. Your team needs solid habits for calling models and handling their output first; LangChain then gives a structure for LLM applications, which RAG grounds in company data and agents extend into multi-step automation.
AI ENGINEERING
AI Engineering with Python Corporate Training
Build Python skills for calling models, handling outputs, testing, and packaging AI features as maintainable code.
BUILD WITH LANGCHAIN
LangChain & LLM Application Development Training
Compose prompts, chains, memory, and tool calls into LLM applications using the LangChain framework.
GROUND WITH RAG
Retrieval Augmented Generation (RAG) Corporate Training
Add retrieval over your own documents to these LangChain apps so responses draw on approved sources.
BUILD AGENTS
Designing & Building Enterprise AI Agents Training
Extend chains into agents that choose tools, plan steps, and act across enterprise systems under control.
Why Choose Edstellar as Your LangChain and LLM Application Development Corporate Training Provider
As your training partner, we make comparing providers simple: share your training needs and we shape a LangChain & LLM Application Development program around your stack, timelines, and delivery mode. You get a matched trainer profile, deep LLM expertise, and a plan your leaders can approve with confidence.
EXPERT TRAINERS
YEARS DELIVERING
ORGANIZATIONS TRAINED
CERTIFIED
Tailored
Content shaped around your LLM application and RAG use cases, so every session maps to your real development needs.
Proven Methodology
5+ years delivering corporate training to applied AI and software engineers across industries and regions.
Practice-Based Learning
5 hands-on labs across 6 modules. Your team builds production LLM applications with instructor guidance.
Expert Trainers
5,000+ certified trainers matched to your LangChain and LLM stack, with a full trainer profile shared before your program is booked.
Global Scale
One LLM application development program across your offices worldwide, in hybrid, onsite, or live virtual.
Measurable Outcomes
Baseline-to-capstone measurement and Skills Matrix reporting keep your LLM capability lift visible.
Build LangChain Applications Through Grounded Retrieval and Observable Agent Workflows
Edstellar develops LLM application skills through connected LangChain exercises. Participants design prompts and chains, build retrieval over documents, and add tools to bounded agent workflows. Testing and tracing inform the final application and deployment plan, with feedback on grounding, failure handling, guardrails, and the evidence used to judge output quality.
- Step 01
Consult
We assess Python and application-development experience, LLM API familiarity, and document-handling needs to identify the cohort's priorities for chains, retrieval, agent tools, and evaluation.
- Step 02
Plan
We establish LangChain concepts before prompts, parsers, and memory, then connect vector-store retrieval, tool-using agents, tracing, and guardrails in the final application.
- Step 03
Align
We align LLM application engineering expertise with software, AI, data, and platform teams, confirming suitable API environments, document samples, and tools for controlled development exercises.
- Step 04
Customize
We adapt retrieval questions and multi-step assistant tasks to familiar use cases, using appropriate sample documents and bounded tool actions to make evaluation and failure cases concrete.
- Step 05
Execute
Participants build chains, connect vector stores, and test tool-using agents, receiving code-review and debugging feedback on grounding, retries, traces, and the behavior of guardrails.
- Step 06
Evaluate
We assess the application and deployment plan for working chain and retrieval paths, controlled tool use, traceable evaluations, and clearly explained limitations and failure-handling decisions.
Also from Edstellar
Talent assessments
Psychometric, behavioral, leadership and 360-degree assessment.
→Training needs analysis
Needs analysis, gap identification, and a training roadmap.
→L&D consulting
Learning strategy, content, technology and ROI measurement.
→OD consulting
Org design, succession planning and cultural change.
→Managed training services
Training outsourcing, vendor management, logistics and administration.
→Coaching solutions
Executive and manager coaching that keeps skills in use.
Start an RFP shaped to your needs.
Answer three quick questions about team size, scope, and how often you need the program run, then send the request from the last step. Everything you choose travels with it, so you only fill this in once.
How many team members need training?
LangChain & LLM Application Development Training Packages & Pricing
Where L&D holds an annual budget, license packages price the whole year at once instead of raising a purchase order per workshop. Hours and licenses are pooled across teams, programs and locations, so unused capacity is not stranded in one department.
Starter
120
trainee licenses64 hours of group training
Tailored for SMBs
Growth
320
trainee licenses160 hours of group training
Ideal for growing SMBs
Enterprise
800
trainee licenses400 hours of group training
Designed for large corporations
Custom
Unlimited
trainee licensesDuration set by the rollout
Multi-region and multi-year
Training Packages & Pricing
Choose the training format that works for your team. Pricing is customized based on your requirements, cohort size, and delivery needs.
Request a Quote for LangChain and LLM Application Development Training for Your Team
Tell us your team size, engineering roles, and preferred delivery mode. We will confirm availability and respond with a tailored training plan covering scope, schedule, and pricing.