LLM Application Engineering with LangChain
Build production-shaped LLM applications: chains, memory, tools, structured output, streaming and error handling — engineered against a mock/real provider seam so everything runs offline in class and swaps to Azure OpenAI or Bedrock with one variable in production.
What this course covers.
6 modules, 39 named skill atoms. Expand any module to see them.
1LLM App Anatomy7 skill atoms
2Memory & State6 skill atoms
3Tools & Function Calling7 skill atoms
4Structured Output6 skill atoms
5Provider Seam Pattern6 skill atoms
6Ship a Working App7 skill atoms
AI-301 in the role journeys.
This course appears in 3 of our 45 role journeys. Here is what a learner takes immediately before and after it in each.
Roles this course serves
This course is authored to band B3.
Every course we run is written to one rung of the CASI ladder, so a plan can be assembled to take a team from where they are to where they need to be.
What do B1–B6 mean?The CASI Capability Ladder — click to expand
Every course targets a band on the CASI Capability Ladder — our six-band proficiency scale, anchored to open standards (O*NET, ESCO, NICE, NIST AI RMF, Bloom's). A band tells you how deep a course goes, and what evidence proves it.
A note on B6. Courses in this catalog target B1–B5. B6 is not taught — it is recognised, through a portfolio and a panel, once someone is setting direction for others. Every journey here is built to land a learner at B5.
Other AI courses at this level.
AI Workflow Automation for Business Teams
Classical Machine Learning with scikit-learn
Feature Engineering & Model Evaluation Mastery
Deep Learning with PyTorch
Computer Vision with CNNs
NLP & Sequence Models
Run AI-301 for your team.
This course runs at several lengths depending on how deep you need to go and how much of it your people already have. Tell us who is being trained and we will scope it.