Building Grounded Language Models
Like Duolingo, but for Building Grounded Language Models. Tomo turns the whole topic into a game you play five minutes a day, until it actually sticks.
For the part of you with thirty open tabs that never became anything.
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Key ideas in Building Grounded Language Models
- LLMs predict the most likely next word based on patterns
- Grounding connects the model to a 'source of truth' outside its weights
- The model prioritizes sounding coherent over checking a database
- Understanding that LLMs predict sequences rather than retrieving stored facts
- It prevents the model from relying solely on its outdated training data
- Training data contains patterns, not a live index of software documentation
- The role of grounding in providing external verification
- Internal knowledge is what the model learned during its initial training
- LLMs predict sequences rather than retrieving stored facts
- External context is the specific data you feed it during a conversation
- A grounded system should admit when the answer is missing from the provided context
- Forcing an answer leads to dangerous hallucinations in engineering tasks
- Refusal to answer is a safety feature, not a failure
- Context must be explicitly provided in the prompt
- Distinguishing between training data and provided context
- Instructions must tell the model to prioritize the pasted text
You've tried the other tabs
Thirty open tabs. Four facts you actually kept.
You watched. You nodded. By Sunday it was gone.
One answer, then back to scrolling.
Eight weeks. You meant to finish. You didn't.
Tomo gives Building Grounded Language Models the Duolingo treatment: levels, streaks, and quick quizzes that test what you just learned. That game loop is what the tabs above never had, so it's the one you actually finish.
Here's what playing it feels like
A real question from this course. Take your best guess.
When we 'ground' an AI model, what are we providing it with?
Get it right to open this lesson and 95 more in the app.
Where Building Grounded Language Models takes you
Learn to bridge the gap between static AI models and the real world by designing robust data retrieval standards and grounding processes.
- 1
First Steps in Grounding
- From Hallucination to Fact
- 2
The Architecture of Truth
- The Retrieval-Augmented Generation (RAG) Loop
- Connecting Models to External APIs
- Designing the Orchestrator Layer
- 3
Data Standards for AI
- Structuring Unstructured Data
- Metadata Standards for Better Search
- Cleaning and Normalizing Real-World Noise
- 4
Retrieval Engineering
- Turning Text into Math (Embeddings)
- Choosing a Vector Database Strategy
- Semantic vs. Keyword Search
- 5
Context Engineering
- The Art of the Context Window
- Chunking Strategies for Long Documents
- Ranking and Re-ranking Results
- 6
Reliability & Verification
- Citations and Source Attribution
- Handling Conflicting Information
- Guardrails for Real-World Data
- 7
Live Data & Tool Use
- Function Calling and Tool Definitions
- Real-time Web Search Integration
- Managing Latency in Data Retrieval
- 8
Scaling & Professional Standards
- Privacy Standards for Sensitive Data
- Versioning Your Knowledge Base
- Monitoring Retrieval Performance
- 9
The Deep Mechanics
- How Vector Similarity Actually Works
- The Future of Dynamic Model Memory
9 sections · 24 units · 96 levels. Built to play, not to enroll.
You pick the voice
Building Grounded Language Models is taught in the The Professor style: clear, structured, thorough. Want a different feel? In the app you can spin up the same topic in any of Tomo's teaching styles. Same facts, totally different vibe.
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