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7 modules
Course
LLM Foundations
Treat an LLM as an engineering system: weights, tokens, context, transformers, training, inference, APIs, grounding, evals, and the harness around the model.
Private Discord community
Practice, quizzes, and a final project
Mentors and checkpoints
Lifetime access and updates
About the courseWithout heavy mathematics, trace one LLM call: text becomes tokens, the transformer processes them, and inference generates a continuation. Then examine the harness around the model, context, sources, and checks. Separate weights, application memory, and external data; compare pretraining, post-training, RAG, and fine-tuning; define the JSON response format, budget, and mini-eval. Finish with an engineering card for one scenario: task, context, source version, model mode, response format, budget, evaluation, and failure behavior.
- Separate model weights, current context, application memory, and external data
- Understand tokens, embeddings, attention, logits, sampling, and inference without heavy math
- Choose between context, RAG, fine-tuning, and memory for a specific problem
- Design an API response format, grounding plan, and quality checks for one LLM scenario
LLM Foundations
Who the course is for
New
Developers who need a working model of context, inference, APIs, and response quality
Pro
Product managers and analysts who use LLM features and need to reason about latency, cost, and model errors
Technical leads who choose models, review structured outputs, and discuss evals with engineering teams
Beginners ready to understand LLMs without heavy mathematics or training a model from scratch
Specialists implementing RAG, structured outputs, and quality evaluation
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Curriculum
Across seven modules, understand one LLM call, API response formats, evals, and the application harness around the model.
01
Model Without Magic
02
Tokens and Context
03
Transformer in Plain Language
04
How Models Are Trained
05
Model, Inference, and API
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Learning Platform
Short lessons, practice, and final artifacts live on one platform. Wallet login does not require a separate password.
- Wallet LoginConnect MetaMask from any device and resume where you stopped. You do not need a separate password.
- Short LessonsLessons are split into small blocks. Complete one in a short session, then resume from the same point later.
- Practice ThroughoutAssignments apply the material right after a lesson. In larger programs, separate outputs build toward a final project or capstone.
- One WorkspaceLessons, quizzes, assignments, and course materials stay together. Technical tasks can use the editor and syntax highlighting.
CommunityEach block ends with an assignment. Keep the intermediate results and assemble the final piece step by step.
Reviews and Community
01
Practice Reviews
Mentors review code, models, product decisions, and research artifacts in a format that fits each program.
02
Expert AMAs
Developers, researchers, and founders answer questions and show how they make decisions in real projects.
03
Research Briefings
The Guide DAO team follows new papers, tools, protocols, and working practices and connects them to the curriculum.
What You Leave With
Finish the program with a verifiable result you can show to a team or use in your next project.
Final Project
Build the program's main result against clear criteria and take it to a state another person can verify.
Portfolio of Work
Keep intermediate artifacts, sources, and decisions so others can see how you worked and why you reached the result.
Next Step
Record the result's limits, open questions, and next actions for your own project, a team, or a new role.