Tech Tuesday-Large Language Models

How Large Language Models Work

Tech Tuesday — Large Language Models (LLMs) turn text you type into predictions of “what comes next.” In the kitchen, that can mean drafting menu plans, rewriting recipes for allergies, or explaining why your sauce split—instantly, in plain language.

What Is It and Why It Matters

LLMs are neural networks trained on massive text corpora to predict the next token (word/subword) given prior context. They enable fluent explanations, structured plans, and step-by-step guidance that feels conversational—useful for home cooks, bloggers, and restaurants standardizing processes.

How It Works (High Level)

Pipeline: your prompt → tokenization → embedding → Transformer layers (self-attention + feed-forward) → logits for next-token → sampling/decoding → iterate until stop.

  • Inputs/data: Text prompts (and optionally images/tools in multimodal setups).
  • Model family & features: Transformer decoder (self-attention) with positional encodings, layer norm, residuals.
  • Outputs & confidence: A probability distribution over the next token; temperature/top-p shape creativity.
  • Human-in-the-loop: You steer via prompts; guardrails and review catch mistakes or hallucinations.

Deep Dive: Model & Data (Tech Tuesday Core)

Feature Signals & Architecture

Embeddings map tokens to vectors; self-attention lets each token weigh relevant context; stacked layers refine representations. The final linear head produces logits per vocabulary token. Training minimizes cross-entropy loss between predicted and true next tokens. Fine-tuning and preference optimization align outputs with human expectations.

Code Example

Toy next-token sampler showing temperature and nucleus (top-p) decoding. This mirrors what real LLMs do after computing logits.

import numpy as np

def softmax(x):
    x = x - np.max(x)
    e = np.exp(x)
    return e / e.sum()

def apply_temperature(logits, temperature=1.0):
    return logits / max(temperature, 1e-6)

def top_p_filter(probs, p=0.9):
    idx = np.argsort(probs)[::-1]
    sorted_probs = probs[idx]
    cum = np.cumsum(sorted_probs)
    cutoff = idx[cum <= p] if (sorted_probs[0] >= p) else idx[:np.searchsorted(cum, p)+1]
    mask = np.zeros_like(probs, dtype=bool)
    mask[cutoff] = True
    filtered = np.where(mask, probs, 0.0)
    return filtered / filtered.sum()

# toy logits for a tiny vocab
vocab = ["salt", "pepper", "olive", "oil", "sugar"]
logits = np.array([2.4, 1.2, 0.7, 2.0, -0.5])

temperature = 0.8
probs = softmax(apply_temperature(logits, temperature))
probs = top_p_filter(probs, p=0.9)

next_token = np.random.choice(vocab, p=probs)
print("Sampled:", next_token)

Evaluation & Metrics

Perplexity (how well the model predicts text), exact/partial match on structured tasks, and human preference scores for helpfulness/safety. Include adversarial tests (tricky prompts), domain tests (culinary terms), and calibration checks (does confidence track accuracy?).

Practical Kitchen Applications

  • Recipe rewrites: Convert “for 4” → “for 10,” metric/imperial swaps, or allergy-safe variants.
  • Menu & shopping plans: Turn a pantry list into a week of meals plus a categorized grocery list.
  • Troubleshooting: Explain failures (curdled custard, dense bread) and give recovery steps.
  • Back-of-house SOPs: Draft standard instructions, batch scaling, and labeling checklists.

Limitations, Privacy, and Safety

LLMs can be confidently wrong (hallucinations), inherit bias from training data, or leak sensitive inputs if logged. Mitigate with human review, on-device or private deployments when possible, and prompt patterns that ask for citations or uncertainty estimates. Keep food-safety calls conservative.

Try It Yourself

Prompt an LLM to: (1) scale a favorite recipe and justify each conversion; (2) propose three substitutions for a missing ingredient; (3) create a prep timeline that sequences oven and stovetop tasks efficiently.

Closing

LLMs are powerful prediction engines, not oracles. Treat them like an eager sous-chef: great at drafts, fast at options—final taste and safety checks are still yours.

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