From Foundations to Deployment
An interactive journey through the core concepts, architectures, and practical applications of modern AI engineering. Explore the building blocks of systems like GPT and learn how to create your own intelligent applications.
Mind Map: AI Engineering Landscape
Explore the high-level overview derived from your NotebookLM map. Click the image to view full-screen.
Part I: The Foundations of Modern LLMs
This section deconstructs the core scientific and engineering principles that power today's large language models. We'll explore the Transformer architecture, from the basic idea of next-token prediction to the sophisticated mechanics of the attention mechanism.
The Transformer Architecture
The core innovation that enabled modern LLMs. By replacing sequential RNNs with a parallelized attention mechanism, it allowed for training models at an unprecedented scale on GPUs.
Tokenization
The process of converting raw text into a sequence of numerical IDs. Modern models use subword tokenization, balancing vocabulary size with the ability to handle unknown words.
Embeddings & Positional Encoding
Tokens are mapped to high-dimensional vectors (embeddings) that capture semantic meaning. A positional encoding is added to give the model a sense of sequence order.
AI Engineering Overview
- Build generative AI systems end-to-end
- Scale with data, infra, and evaluation
- Interdisciplinary: AI, data, product, UX
Understanding Foundation Models
- Evolution from LLMs to multimodal FM
- General-purpose capabilities via pretraining
- Adaptation with prompting and fine-tuning
Probabilistic Nature of AI
- Sampling process drives variability
- Open-ended outputs: creativity & hallucination
- Control via temperature, top-k, nucleus
The Attention Mechanism: Q, K, V Explained
The core of the Transformer is self-attention, which allows the model to weigh the importance of different words in a sequence. This is achieved using three learned vectors: Query, Key, and Value. Click on each component to learn its role.
Query (Q)
Represents the current word's "question" about the context. It's what the word is looking for.
Key (K)
Represents what a word "offers." It's like a label or index for the information in that token.
Attention Score
A high dot-product score between a Query and a Key indicates high relevance.
Value (V)
Represents the actual content or information of the word.
How it Works
The model calculates an attention score by comparing the **Query** of the current word with the **Key** of every other word. These scores are then used to create a weighted sum of all the **Value** vectors, producing a new, context-aware representation for the current word. Click on Q, K, or V to see a detailed explanation.
Part II: Building Applications with LLM APIs
This section transitions from theory to practice, focusing on the essential skills for programmatically controlling pre-trained models. We'll cover API interaction, advanced prompt engineering, and the powerful technique of tool calling.
Interactive Flow: The Tool Calling Loop
Tool calling allows LLMs to interact with external systems. It's a multi-step conversation between your app and the model. Click each step to understand the flow.
Step 1: Request with Tools
The developer sends a prompt to the model, including a list of available `tools` defined by a JSON schema. This tells the model what functions it can potentially use.
Structured Outputs: JSON and Schemas
Modern models support constrained generation for predictable outputs. Prefer JSON mode and function/tool schemas to reduce parsing errors and improve reliability.
JSON Mode
Ask the model to return strict JSON. Validate and reject malformed responses; retry with a shorter context if needed.
Function Schemas
Define `tools` with JSON Schema. Models select a function and emit typed arguments, lowering post-processing complexity.
Schema Tips
- Mark required fields in `required`
- Use enums for finite choices
- Bound numbers with `minimum`/`maximum`
- Prefer small, composable functions
Sampling Strategies
- Greedy / Logits: Deterministic best-token decoding; low diversity.
- Temperature: Scales logits; lower is more deterministic.
- Top-k / Top-p: Limit candidate set by count or cumulative mass.
- Beam search: Multiple hypotheses; costly and less used for open text.
- Constrained / JSON mode: Enforce schema for structured outputs.
Prompt Engineering
- Clarity: Roles, goals, constraints, and examples.
- Context: Provide retrieved snippets and metadata.
- Defensive: Refusal guidance, jailbreak-resistant patterns.
- Tool-first: Encourage calling functions with schemas.
- Chain-of-thought (hidden): Use internal reasoning, keep summaries external.
Part III: Engineering Intelligent Systems
Here, we explore how to construct complex systems that can perform multi-step tasks. This involves architecting "agents," grounding them in factual data with RAG, and designing multi-agent systems for collaboration.
The RAG Pipeline: Grounding LLMs in Your Data
Retrieval-Augmented Generation (RAG) prevents hallucinations by giving the LLM an "open book" of your data to consult before answering. Explore the three main stages of the pipeline.
1. Indexing
Documents are split into chunks, converted to vector embeddings, and stored in a vector database.
2. Retrieval
The user's query is embedded, and a similarity search finds the most relevant chunks from the database.
3. Generation
The retrieved chunks are added to the prompt, giving the LLM context to generate a grounded answer.
RAG Best Practices
Chunking
Use semantic or heading-aware chunking with modest overlap (e.g., 128–256 tokens) to preserve coherence without duplication.
Hybrid Search
Combine dense vectors with keyword/BM25 to capture exact terms, numbers, and symbols.
Reranking
Apply cross-encoder rerankers on top-K candidates to improve precision while keeping latency manageable.
Filters & Metadata
Index rich metadata (source, section, date) and filter by scope/time to reduce noise.
Citations
Return source URLs/IDs with spans. Encourage grounded answers and enable auditability.
Evaluation
Measure retrieval precision/recall, answer faithfulness, and context sensitivity on a task-specific test set.
Multi-Agent Frameworks: A Comparison
To build systems where multiple agents collaborate, developers use frameworks that manage their interaction. The choice of framework involves a trade-off between control and ease of use.
| Feature | LangGraph | CrewAI |
|---|---|---|
| Core Abstraction | State Graphs (Nodes & Edges) | Agents, Tasks, Crews |
| Control Level | Low-level, explicit control over state and transitions. | High-level, abstracts away orchestration. |
| Ease of Use | Steeper learning curve; requires graph theory concepts. | Beginner-friendly; intuitive role-playing paradigm. |
| Ideal Use Case | Complex, production-grade systems with custom control flows. | Rapid prototyping of collaborative agent teams. |
AI Agents
Components
- Planner
- Tools & Environment
- Memory & State
Planning & Tool Use
- Decompose tasks to toolable steps
- Validate preconditions and outputs
- Fallback paths and retries
Memory Systems
- Short-term: chat window/context
- Long-term: vector store/user profile
- Episodic/task memory: artifacts
Part IV: Advanced Customization & Evaluation
To build truly effective applications, we must customize models for specific tasks and rigorously evaluate their performance. This section covers fine-tuning with LoRA and the critical frameworks for agent evaluation.
Fine-Tuning Efficiency: LoRA vs. Full Fine-Tuning
Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA drastically reduce the computational cost of adapting models. This chart visualizes the difference in trainable parameters for a typical large model.
Evaluating Agent Performance
Evaluating agents requires looking beyond simple accuracy. A robust framework assesses the entire process, from tool use to response quality. Key metrics include:
- ✓ Task Performance: Success rate, accuracy, error rate.
- ✓ Efficiency: Cost (tokens consumed) and latency.
- ✓ Trajectory Evaluation: The sequence of actions, including tool use precision and recall.
- ✓ Safety & Ethics: Checks for bias and harmful content.
Preference Optimization
- RLHF: Policy optimized with a reward model; high quality but complex.
- DPO/ORPO: Direct optimization from preference pairs; simpler training, strong results.
- Self-Play/Constitutional: Model critiques and revises outputs using principles to reduce human labeling.
Latency & Cost Optimizations
- Speculative Decoding: Draft model proposes tokens; larger model verifies for speedups.
- Caching: Reuse embeddings, retrieval results, and prompt prefixes; apply ETags for HTTP resources.
- Distillation: Train smaller student models for inference while preserving quality.
Advanced Fine-Tuning & Data
- PEFT techniques: LoRA, QLoRA, adapters for efficiency.
- Model merging: Blend specialist checkpoints carefully.
- Curation: High-quality instruction/preference pairs.
- Synthesis: Generate data with guardrails and review.
Inference Optimization
- Metrics: Latency (p50/p95), throughput, cost.
- Compression: Quantization, pruning, KV cache reuse.
- Service level: Batching, streaming, autoscaling.
Part V: Multimodal AI & Deployment
The final frontier is expanding beyond text and making our applications accessible to the world. This section covers voice and image generation, and the essentials of deploying apps with platforms like Hugging Face and Gradio.
Deployment in 5 Steps
Using modern tools like Gradio and Hugging Face Spaces, deploying a live AI demo has never been easier. The process abstracts away complex web infrastructure.
Write Script
Create `app.py` with Gradio UI.
Define Deps
List libraries in `requirements.txt`.
Create Space
On Hugging Face, select Gradio SDK.
Push Code
Use Git to upload your files.
Go Live!
Your app is automatically deployed.
Production Ops
Operational excellence keeps LLM systems reliable and affordable. Track quality, cost, and safety with rigorous monitoring.
Observability
- Structured logs with request IDs
- Traces across retrieval, tools, and model calls
- Prompt/version tracking
Cost Control
- Token and tool budgets per request
- Early exit and truncation policies
- Autoscaling and request batching
Reliability
- Retries with backoff and idempotency keys
- Circuit breaking on upstream errors
- Chaos testing for tool failures
User Feedback Loop
- Collect thumbs up/down and free-form feedback
- Capture input, retrieval context, and tool traces
- Triage to datasets for supervised or preference tuning
Safety & Governance
Bake-in safeguards and continuous evaluation to mitigate risks while maintaining utility.
Runtime Controls
- Input/output filters and PII redaction
- Tool allowlists and rate limits
- Safety classifiers with blocklists and context-aware rules
Red Teaming & Audits
- Adversarial prompts and jailbreak testing
- Dataset audits for bias and leakage
- Post-incident reviews and action tracking