Learn how large language models work at a practical level, including tokens, transformers, prompting, RAG, fine-tuning and evaluation. For Generative AI & Large Language Models (LLMs) Basics Guide, the examples stay small enough for you to predict the result before execution. This page targets the practical intent behind language models, not repetition of the phrase itself.
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Understand the computation behind Generative AI & Large Language Models (LLMs) Basics Guide
A company policy assistant can retrieve the most relevant policy sections, pass them to an LLM with citation instructions and refuse to answer when the retrieved evidence is insufficient.
- Large language models predict token sequences from context and can generate text, code and structured outputs.
- Prompting changes the context provided to the model, while fine-tuning changes model parameters and retrieval adds external information.
- Hallucinations are plausible but unsupported outputs, so high-stakes workflows need grounding, verification and clear fallbacks.
- Agentic systems add tools and multi-step control, which increases usefulness but also expands the surface for errors and permissions mistakes.
Check shapes and training signals with a tiny model
For Generative AI & Large Language Models (LLMs) Basics Guide, predict one visible result before each snippet runs—a row count, shape, statistic, ranking or error pattern—then compare it with the printed checkpoint.
# Generative AI & Large Language Models (LLMs) Basics Guide · example 1
prompt = '''You are given three customer notes.
Return JSON with: issue, urgency, next_action.
Do not invent facts that are not present.'''
print(prompt)Generative AI & Large Language Models (LLMs) Basics Guide checkpoint 1: Run the cell and inspect the printed values before changing anything.
# Generative AI & Large Language Models (LLMs) Basics Guide · example 2
import numpy as np
X = np.random.default_rng(42).normal(size=(32, 8)).astype('float32')
y = (X[:,0] + X[:,1] > 0).astype('float32')
print('X:', X.shape, 'y:', y.shape)Generative AI & Large Language Models (LLMs) Basics Guide checkpoint 2: X: (32, 8) y: (32,)
# Generative AI & Large Language Models (LLMs) Basics Guide · example 3
import tensorflow as tf
model = tf.keras.Sequential([
tf.keras.layers.Input(shape=(8,)),
tf.keras.layers.Dense(16, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.summary()Generative AI & Large Language Models (LLMs) Basics Guide checkpoint 3: The summary lists layer output shapes and parameter counts before training.
# Generative AI & Large Language Models (LLMs) Basics Guide · example 4
history=model.fit(X,y,epochs=5,batch_size=8,validation_split=.25,verbose=0)
print({k: round(v[-1],3) for k,v in history.history.items()})Generative AI & Large Language Models (LLMs) Basics Guide checkpoint 4: Final training and validation metrics are printed side by side.
# Generative AI & Large Language Models (LLMs) Basics Guide · example 5
p=model.predict(X[:5],verbose=0).ravel()
print(np.round(p,3))
print('labels:', (p>=.5).astype(int))Generative AI & Large Language Models (LLMs) Basics Guide checkpoint 5: Probabilities are shown before thresholding, making uncertain predictions visible.
Diagnose validation behavior and uncertain predictions
Hallucinations are plausible but unsupported outputs, so high-stakes workflows need grounding, verification and clear fallbacks.
Agentic systems add tools and multi-step control, which increases usefulness but also expands the surface for errors and permissions mistakes.
Self-check with answers
For Generative AI & Large Language Models (LLMs) Basics Guide, what result should you be able to explain without the code?
Large language models predict token sequences from context and can generate text, code and structured outputs.
Which assumption matters most in this Generative AI & Large Language Models (LLMs) Basics Guide exercise?
Hallucinations are plausible but unsupported outputs, so high-stakes workflows need grounding, verification and clear fallbacks.
What failure mode would make this Generative AI & Large Language Models (LLMs) Basics Guide result untrustworthy?
Agentic systems add tools and multi-step control, which increases usefulness but also expands the surface for errors and permissions mistakes.
Keep the experiment small enough to reproduce
Try it yourself workspace
Edit this Generative AI & Large Language Models (LLMs) Basics Guide starter, copy it, and run it in the downloadable notebook or your local Jupyter environment.
Questions specific to this lesson
What should I establish before tuning Generative AI & Large Language Models (LLMs) Basics?
For Generative AI & Large Language Models (LLMs) Basics Guide, begin with a leakage-safe split and a simple baseline so later tuning has a credible reference point.
How do I check overfitting in Generative AI & Large Language Models (LLMs) Basics Guide?
For Generative AI & Large Language Models (LLMs) Basics Guide, compare training and validation behavior, inspect held-out errors, and repeat the evaluation with a stable split or cross-validation when appropriate.
Which metric should I report for Generative AI & Large Language Models (LLMs) Basics Guide?
Agentic systems add tools and multi-step control, which increases usefulness but also expands the surface for errors and permissions mistakes.
How should I explain Generative AI & Large Language Models (LLMs) Basics Guide in a portfolio or interview?
When presenting Generative AI & Large Language Models (LLMs) Basics Guide, show the problem definition, baseline, split strategy, metric, one error-analysis finding and one limitation.
Primary references and further reading
For Generative AI & Large Language Models (LLMs) Basics Guide, use these primary references when exact syntax, defaults or formal definitions matter beyond the examples on this page.
