ReAct pattern in Prompt Engineering / GenAI - Model Pipeline Trace

Model Pipeline - ReAct pattern

The ReAct pattern helps AI models think and act step-by-step. It combines reasoning and actions to solve problems better.

Data Flow - 4 Stages

1Input Question

1 question string→Receive user question→1 question string

"What is the capital of France?"

↓

2Reasoning Step

1 question string→Model thinks internally about the question→1 reasoning string

"The capital of France is a famous city known for the Eiffel Tower."

↓

3Action Step

1 reasoning string→Model performs an action like searching or recalling knowledge→1 action result string

"Retrieved: Paris is the capital of France."

↓

4Answer Generation

1 action result string→Model combines reasoning and action to produce final answer→1 answer string

"The capital of France is Paris."

Training Trace - Epoch by Epoch

Loss
0.9 | *
0.8 | **
0.7 | ***
0.6 | ****
0.5 | *****
0.4 | ******
0.3 | *******
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	0.45	Model starts learning to combine reasoning and actions.
2	0.65	0.60	Loss decreases as model improves reasoning steps.
3	0.50	0.72	Model better integrates actions with reasoning.
4	0.38	0.82	Clear improvement in answer accuracy.
5	0.30	0.88	Model converges with strong reasoning-action balance.

Prediction Trace - 4 Layers

Layer 1: Input Question

Layer 2: Reasoning Step

Layer 3: Action Step

Layer 4: Answer Generation

Model Quiz - 3 Questions

What does the 'Action Step' in the ReAct pattern do?

A Receives the user question

B Performs a task like searching or recalling information

C Generates the final answer directly

D Evaluates model accuracy

Key Insight

The ReAct pattern improves AI by making it think step-by-step and act based on reasoning. This leads to better answers and more reliable problem solving.