Code generation agent design in Agentic Ai - Model Pipeline Trace

Model Pipeline - Code generation agent design

This pipeline shows how a code generation agent learns to write code from examples. It starts with raw code data, processes it, trains a model to predict code, and improves its accuracy over time. Finally, it generates new code based on input prompts.

Data Flow - 6 Stages

1Raw code dataset

10000 code snippets x 1 column (code text)→Collect raw code examples from various sources→10000 code snippets x 1 column (code text)

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

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2Preprocessing

10000 code snippets x 1 column→Tokenize code into sequences of tokens→10000 sequences x 50 tokens each

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

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3Feature Engineering

10000 sequences x 50 tokens→Convert tokens to numeric IDs and pad sequences→10000 sequences x 50 integers

[12, 45, 3, 7, 2, 8, 4, 1, 7, 3, 9, 8, 0, 0, 0, ...]

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4Model Training

8000 sequences x 50 integers (train set)→Train a transformer-based model to predict next token→Trained model with learned weights

Model learns to predict 'b' after 'a +' in code

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5Validation

2000 sequences x 50 integers (validation set)→Evaluate model loss and accuracy on unseen data→Validation loss and accuracy metrics

Loss=0.15, Accuracy=0.92

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6Code Generation

Prompt sequence x 50 integers→Generate code tokens step-by-step using model→Generated code sequence x 50 tokens

Input: 'def multiply(a, b):' Output: 'return a * b'

Training Trace - Epoch by Epoch

Epoch: 1 2 3 4 5
Loss: 1.2-0.85-0.55-0.35-0.20
     *  *   *   *   *
    *   *   *   *
   *    *   *
  *     *
 *

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Model starts learning basic token patterns
2	0.85	0.65	Model improves understanding of code syntax
3	0.55	0.78	Model captures common code structures
4	0.35	0.88	Model generates more accurate next tokens
5	0.20	0.93	Model converges with high accuracy

Prediction Trace - 5 Layers

Layer 1: Input token embedding

Layer 2: Transformer encoder layers

Layer 3: Next token prediction (softmax)

Layer 4: Token selection

Layer 5: Sequence generation

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 fluctuates randomly

Key Insight

This visualization shows how a code generation agent learns by converting raw code into tokens, training a model to predict the next token, and improving accuracy over time. Tokenization and stepwise prediction are key to generating meaningful code.