Getting Started Recap
What building AI platforms actually entails
- Not just coding - design, architecture, and engineering decisions
- Complex projects need multi-layered AI architectures, data transformations, agents, ambient intelligence
- Novel applications require architecture designed for that specific problem
- Architecture choices define what's possible
The Cost Reality
- AI API usage measured in tokens (MToks)
- Input and output tokens priced differently (output typically costs more)
- Context window size affects both price and performance
- Applications generating large AI responses incur higher costs
- Need to manage token counts sent to API
Hallucinations are a Feature, Not a Bug
- Scale of hallucinations and downstream effects expand exponentially with poor architecture
- Many hallucinations can be stamped out, accounted for, or predicted through design
- Requires persistence, trial and error, systems-thinking
- Same mechanism that causes hallucinations enables creativity and variation
Tech Stack Doesn't Matter
- Architectural concepts apply regardless of stack
- Security critical: never expose API keys on frontend
- All LLM API calls happen on backend
Setup and Testing Recap
Pick an LLM and learn by doing
- Different LLMs have varying strengths and textual voices
- Learn through iteration on system prompts, not just benchmarks
- Consider multi-LLM strategy: expensive models for complex tasks, cheaper models for simple tasks
- Claude 4.0 for heavy analysis, Mistral for simple tasks
Workbench Anatomy
- System prompt: static instruction defining AI behavior and context
- User request: specific data for this particular instance
- Test and iterate before implementing in application
- Use prompt library to organize high-volume testing
Setup Creates Iteration Foundation
- Frontend, backend, database, mobile - get environment working first
- AI-powered IDEs (Cursor) write code
- Your job: understanding architecture, overseeing, troubleshooting
- Learn coding through process, not syntax memorization
Prompt Engineering Recap
Prompts are control mechanisms
- Vague prompts → AI invents interpretations
- Contradictory prompts → AI picks randomly
- Well-structured prompts → consistent behavior
- Difference between working product and broken one comes down to prompt quality
Modular Structure
- Core Identity: what is this AI, what does it do
- Platform Specifics: context about where/how it operates
- Understanding Role: scope, responsibilities, boundaries
- Dissecting Requests: how to parse incoming data
- Response Expectations: exact output format
- Quality Standards: non-negotiable benchmarks
- Each module handles one concern, update independently
Structure Requests and Responses
- Requests: key-value pairs (
user-input: "text",convo-summary: "summary") - Responses: predefined functions with strict argument types
- Examples:
speak("dialogue"),attack(damage, 20),try("outcome1", "outcome2", 100/1000) - Structured outputs aid parsing, prevent hallucinations, maintain logical coherence
Minimize to Essentials
- Input only what you need for expected output
- Setup prioritization hierarchy:
user-input > convo-history > saved-prefs - Output only necessities
- More elements = higher chance of confusion
- Build → reduce → build → reduce
Define and Enforce Rules
- Critical rules: ALL CAPS, repetition, strategic placement
- Eliminate contradictory instructions
- Be explicit about constraints (what AI cannot do)
- Define argument types strictly and repeat them
- Example bad rule: "Respond appropriately"
- Example good rule: "You must respond using only the functions defined in Response Expectations Module. Do not invent new functions or arguments."
Preventing Hallucinations
- Restrict AI to specific formatted responses
- Reiterate: cannot make up own functions or arguments
- Be hyper-specific about requirements
- Identify and eliminate contradictory prompting
- ALL CAPS to stress critical aspects
Test and Iterate
- All LLMs react differently to certain prompting
- Testing, iterating, saving updates essential
- Consider model size in proportion to task size
- Massive response with complex logic needs bigger (more expensive) model
Multilayered Architectures Recap
- Multiple coordinated prompts compound transformations substantially
- Layer types: Correction, Reasoning/Strategy, Memory Consolidation, Content, Catch-All
- System types: Cyclical (same flow), Circumstantial (adaptive), Hybrid (most real-world systems)
- Backend handles: true randomness, deterministic calculations, unbiased judgment, data persistence
- Tradeoffs: More layers = higher cost and latency, but better quality when properly configured
- Correction layers must come before memory consolidation to prevent error compounding
Quiz
This quiz is designed to help with retention. Feel free to skip if you prefer, or use the "Reveal Answer" button to check your understanding.