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pages/techniques.html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>AI Techniques - Cheat Sheet</title>
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<link rel="stylesheet" href="../css/style.css">
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</head>
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<body>
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<nav>
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<div class="nav-inner">
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<a href="../index.html" class="nav-brand">AI Cheat Sheet</a>
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<div class="nav-links">
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<a href="terminology.html">Terminology</a>
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<a href="techniques.html" class="active">Techniques</a>
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<a href="use-cases.html">Use Cases</a>
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<a href="model-types.html">Model Types</a>
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<a href="prompts.html">Prompt Guide</a>
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<a href="math.html">Math & Concepts</a>
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</div>
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</div>
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</nav>
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<div class="hero">
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<h1>AI Techniques</h1>
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<p>How AI models are built, trained, and optimized.</p>
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</div>
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<div class="container">
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<h2 class="section-title">Training Techniques</h2>
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<div class="def-card">
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<span class="category">Training</span>
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<h3>Backpropagation</h3>
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<p>The core algorithm for training neural networks. It calculates the gradient of the loss function with respect to each weight by chain rule, then adjusts weights to minimize error.</p>
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<div class="example"><strong>Analogy:</strong> Like adjusting a radio dial — you turn it slightly, check if the signal is clearer, and keep adjusting in the right direction.</div>
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</div>
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<div class="def-card">
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<span class="category">Training</span>
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<h3>Epoch</h3>
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<p>One complete pass through the entire training dataset. Models typically train for many epochs.</p>
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</div>
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<div class="def-card">
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<span class="category">Training</span>
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<h3>Batch Size</h3>
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<p>The number of training examples processed before the model's weights are updated. Larger batches are more stable but use more memory.</p>
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</div>
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<div class="def-card">
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<span class="category">Training</span>
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<h3>Learning Rate</h3>
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<p>A hyperparameter that controls how much to adjust weights during each update. Too high → unstable training; too low → slow convergence.</p>
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</div>
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<div class="def-card">
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<span class="category">Training</span>
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<h3>Transfer Learning</h3>
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<p>Using a model trained on one task as the starting point for a model on a second task. Saves time and data.</p>
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<div class="example"><strong>Example:</strong> A model trained on Wikipedia text is fine-tuned for legal document analysis.</div>
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</div>
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<div class="def-card">
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<span class="category">Training</span>
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<h3>Data Augmentation</h3>
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<p>Artificially expanding a training dataset by applying transformations (e.g., rotation, flipping, synonym replacement) to create new training examples.</p>
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</div>
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<h2 class="section-title">Alignment & Improvement</h2>
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<div class="def-card">
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<span class="category">Alignment</span>
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<h3>RLHF (Reinforcement Learning from Human Feedback)</h3>
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<p>A technique to align model outputs with human preferences. Humans rank model responses, and a reward model is trained on those rankings. The main model is then fine-tuned to maximize the reward.</p>
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<div class="example"><strong>Used by:</strong> ChatGPT, Claude, and other conversational AI systems to make them more helpful and harmless.</div>
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</div>
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<div class="def-card">
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<span class="category">Alignment</span>
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<h3>SFT (Supervised Fine-Tuning)</h3>
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<p>Fine-tuning a model on a dataset of input-output pairs to teach it a specific format or style of response.</p>
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<div class="example"><strong>Example:</strong> Training a model to respond in JSON format for API integration.</div>
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</div>
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<div class="def-card">
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<span class="category">Alignment</span>
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<h3>Prompt Tuning</h3>
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<p>Instead of changing model weights, carefully crafting prompts to guide the model's behavior. Zero-cost and reversible.</p>
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</div>
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<div class="def-card">
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<span class="category">Alignment</span>
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<h3>LoRA (Low-Rank Adaptation)</h3>
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<p>An efficient fine-tuning technique that adds small trainable matrices to a frozen pre-trained model, drastically reducing compute and memory needs.</p>
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</div>
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<h2 class="section-title">Deployment & Optimization</h2>
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<div class="def-card">
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<span class="category">Optimization</span>
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<h3>Quantization</h3>
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<p>Reducing the precision of model weights (e.g., from 32-bit to 8-bit) to shrink model size and speed up inference with minimal accuracy loss.</p>
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<div class="example"><strong>Example:</strong> A 13GB model quantized to 4-bit becomes ~3.5GB, fitting on consumer GPUs.</div>
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</div>
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<div class="def-card">
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<span class="category">Optimization</span>
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<h3>Distillation</h3>
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<p>Training a smaller "student" model to mimic the behavior of a larger "teacher" model, capturing its knowledge in a more compact form.</p>
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</div>
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<div class="def-card">
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<span class="category">Optimization</span>
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<h3>Speculative Decoding</h3>
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<p>Using a small model to draft multiple tokens, then having the large model verify them in parallel — speeding up generation.</p>
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</div>
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<div class="def-card">
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<span class="category">Architecture</span>
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<h3>RAG (Retrieval-Augmented Generation)</h3>
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<p>Augmenting a language model with an external knowledge retrieval step. The model first searches a knowledge base, then generates a response using both the retrieved info and its own training.</p>
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<div class="example"><strong>Example:</strong> A customer support bot that searches your product docs before answering questions — no fine-tuning needed.</div>
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</div>
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<div class="def-card">
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<span class="category">Architecture</span>
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<h3>Agent / Tool Use</h3>
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<p>Giving an LLM the ability to call external tools (search, calculators, APIs) to accomplish multi-step tasks.</p>
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<div class="example"><strong>Example:</strong> An AI that searches the web, summarizes results, and writes a report — all autonomously.</div>
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</div>
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<div class="def-card">
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<span class="category">Architecture</span>
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<h3>Chain-of-Thought</h3>
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<p>Asking a model to show its reasoning step-by-step before giving an answer. Dramatically improves performance on reasoning tasks.</p>
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<div class="example"><strong>Prompt:</strong> "Let's think step by step. First, ..."</div>
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</div>
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</div>
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<footer>AI Cheat Sheet — A learning reference for artificial intelligence</footer>
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</body>
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</html>
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