AI Reasoning Pipeline
| Stage | Component | Purpose |
|---|---|---|
| 1. Data Layer | Data | Define schemas, relationships, and data structure |
| 2. Model Layer | GenAI | Core neural architecture and training |
| 3. Reasoning Layer | Reasoning | Logical operations and inference capabilities |
| 4. Feedback Layer | RLHF | Human preference learning and alignment |
| 5. Evaluation Layer | Eval | Performance measurement and quality assurance |
Lifecyle of Frontier AI vs Traditional ML
Traditional ML: Train a single model end-to-end for a specific task
Data → Model Architecture → Training → Evaluation → Deploy
Frontier LLM: Customize pretrained foundation models through multi-stage alignment
Base Model Selection
↓
Instruction Fine-tuning (~1M examples)
↓
Reward Model Training (~100K preferences)
↓
RLHF Optimization (~100K prompts)
↓
RLVR (for reasoning, ~millions of attempts)
↓
Multiple evaluation stages
↓
Deploy with prompt engineering/RAG
Phase 1: Ideation & Design #
| Aspect | Traditional ML | Frontiner LLM |
|---|---|---|
| Starting point | Define task, collect data from scratch | Select pretrained base model |
| Main question | “What features predict Y?” | “Which foundation model to build on?” |
| Data needs | Collect labeled training data | Select/acquire base model + design post-training data |
| Model architecture | Design custom architecture | Foundation model already exists |
| Roles | ML Engineer designs everything | AI Engineer selects & customizes |
Key Insight:
- Traditional ML: Build from scratch
- Frontier LLM: Start with billion-parameter pretrained model
Phase 2: Development & Training #
| Aspect | Traditional ML | Frontiner LLM |
|---|---|---|
| Training approach | Single-stage training on task-specific data | Multi-stage post-training pipeline |
| Stages | 1. Train model on labeled data | 1. Instruction Fine-tuning (SFT) 2. Reward Model training 3. RLHF/Preference tuning 4. RLVR (for reasoning) |
| Data scale | 1K - 1M examples | 100K - 10M+ examples across stages |
| Training objective | Task loss (e.g., cross-entropy) | Multiple objectives: next-token → Q&A format → human preferences → verifiable rewards |
| Compute | Hours to days on GPUs | Days to weeks on massive GPU clusters |
| What’s learned | Task-specific patterns | Eliciting latent capabilities from base model |
Key Insight from RLHF Book:
“Post-training can be summarized as a many-stage training process using three optimization methods: (1) Instruction Fine-tuning, (2) Preference Fine-tuning (PreFT), (3) RLVR”
“Modern versions of post-training involve many, many more model versions and training stages… numerous training iterations before convergence”
Phase 3: Evaluation & Testing #
| Aspect | Traditional ML | Frontiner LLM |
|---|---|---|
| Evaluation scope | Single task metrics | Multi-domain benchmarks (knowledge, reasoning, coding, safety) |
| Benchmarks | Task-specific test set | Standardized suites: MMLU, GPQA, HumanEval, MATH, etc. |
| Evaluation methods | Offline metrics only | 4-layer evaluation: 1. Offline benchmarks 2. LLM-as-a-judge 3. Human evaluation 4. Online A/B testing |
| Contamination concern | Low | CRITICAL - test data may leak into training |
| Evolution | Static benchmarks | Benchmarks saturate rapidly → need new harder ones |
Key Insight from RLHF Book Ch17:
“Evaluation for RLHF has gone through distinct phases: Early chat-phase → Multi-skill era → Reasoning & tools”
“Benchmarks approaching 100% saturation become less reliable… necessitates creating perturbed versions”
Phase 4: Deployment #
| Aspect | Traditional ML | Frontiner LLM |
|---|---|---|
| Deployment unit | Single trained model | Base + post-training checkpoints |
| Inference | Forward pass | Complex generation with sampling, special tokens |
| Customization | Retrain or fine-tune | Prompt engineering, RAG, lightweight fine-tuning |
| Infrastructure | Standard ML serving | Massive inference clusters + caching |
| Cost | Low to moderate | High ($0.01-$10 per request) |
Key Insight from Art of AI Product Dev:
“AI engineers focus on integrating AI models into real-world applications… prompt engineering, API integration”
“Most teams will start with AI engineering [not ML engineering]. As your product matures, you can consider… training your own models”
Phase 5: Production & Monitoring #
| Aspect | Traditional ML | Frontiner LLM |
|---|---|---|
| What to monitor | Model drift, accuracy | Hallucinations, safety violations, user satisfaction |
| Failure modes | Accuracy degradation | Hallucinations, harmful content, prompt injection |
| User feedback | Click-through, conversions | Thumbs up/down, human ratings, red teaming |
| Retraining | Periodic model updates | Continuous post-training with new data |
Phase 6: Continuous Optimization #
| Aspect | Traditional ML | Frontiner LLM |
|---|---|---|
| Improvement method | Collect more labeled data → retrain | Multiple paths: • Prompt engineering • RAG integration • Domain fine-tuning • Additional post-training stages |
| Speed | Weeks to months | Hours (prompting) to weeks (fine-tuning) |
| Cost | Data labeling + compute | Synthetic data generation + post-training |
| Data needs | More task labels | Preference data, synthetic data, distillation |