LangChain: Foundations
Apply LangChain through capabilities, limitations, responsible use.
Advanced / Structured learning draft / Version 0.9
Structured learning draftBuilding AI Agents & LLM Apps
Build a responsible AI workflow for a real work task. You will finish with a documented prompt, verification and revision workflow.
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AI toolsTool stack
LangChainOpenAI
12guided lessons
4focused modules
1reviewable project
PDFoffline workbook
The problem
What this course helps you do
Building AI Agents & LLM Apps is a structured, practical course covering LangChain, AutoGPT, Agents. It emphasizes explainable methods, checked work and a final outcome that can be reviewed.
Who it is for
People who want dependable, responsible AI-assisted workflows.
Prerequisites
- Basic computer and web skills
Portfolio-ready project
Produce a reviewable Building AI Agents & LLM Apps project using LangChain, AutoGPT, Agents.
Deliverable: A working Building AI Agents & LLM Apps artefact plus an evidence-based self-review.
Practice fileResponsible AI workflowA source, instruction, review and privacy-check worksheet.Planning template · MarkdownProject briefDefine the user, outcome, boundaries, evidence and acceptance criteria before building.Printable workbook · PDFComplete course workbookA4-ready lesson prompts, project worksheets, evidence logs and references.
Downloads open without an account. Practice data is fictional; review each file before using it in another system.
Definition of done
- The output matches the stated outcome.
- Inputs and decisions are reproducible.
- Boundary and failure evidence is included.
- Limitations and responsibility considerations are explicit.
Course plan
From first concept to finished work
AutoGPT: Working methods
Apply AutoGPT through clear instructions, context design, verification.
Agents: Applied workflows
Apply Agents through research, creation, automation.
LangChain: Quality and review
Apply LangChain through evaluation, privacy, repeatable practice.
Source-led learning
Authoritative references
Course claims are grounded in primary documentation and recognized public guidance. Recheck version-sensitive information before professional use.