Code generation in Prompt Engineering / GenAI - Model Pipeline Trace

Model Pipeline - Code generation

This pipeline shows how a code generation model learns to write code from examples. It starts with raw code data, processes it, trains a model to predict code tokens, and finally generates new code snippets.

Data Flow - 4 Stages

1Raw code dataset

10000 code snippets x variable length→Collect raw code examples from repositories→10000 code snippets x variable length

def add(a, b): return a + b

↓

2Tokenization

10000 code snippets x variable length→Split code into tokens (words, symbols)→10000 sequences x 50 tokens (max length)

["def", "add", "(", "a", ",", "b", ")", ":", "return", "a", "+", "b"]

↓

3Train/test split

10000 sequences x 50 tokens→Split data into 8000 training and 2000 testing sequences→Training: 8000 x 50 tokens, Testing: 2000 x 50 tokens

Training example: ["def", "add", "(", "a", ",", "b", ")", ":", "return", "a"]

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4Model training

8000 sequences x 50 tokens→Train transformer-based model to predict next token→Trained model with learned token probabilities

Input: ["def", "add", "(", "a", ","] -> Output: "b"

Training Trace - Epoch by Epoch

Epoch 1: 2.3 *****
Epoch 2: 1.8 ****
Epoch 3: 1.4 ***
Epoch 4: 1.1 **
Epoch 5: 0.9 *
(Loss decreases over epochs)

Epoch	Loss ↓	Accuracy ↑	Observation
1	2.3	0.25	Model starts learning basic token patterns
2	1.8	0.40	Loss decreases, accuracy improves as model learns syntax
3	1.4	0.55	Model captures common code structures
4	1.1	0.65	Better prediction of tokens in code sequences
5	0.9	0.72	Model converges with good token prediction accuracy

Prediction Trace - 4 Layers

Layer 1: Input token embedding

Layer 2: Transformer layers

Layer 3: Output token probabilities

Layer 4: Token selection

Model Quiz - 3 Questions

What happens to the loss value as training progresses?

A It decreases steadily

B It increases steadily

C It stays the same

D It randomly jumps up and down

Key Insight

This visualization shows how a code generation model learns token patterns from code examples. The loss decreases and accuracy improves as the model better predicts the next code token, enabling it to generate meaningful code snippets.