Combining retrieved context with LLM in Prompt Engineering / GenAI - Model Pipeline Trace

Model Pipeline - Combining retrieved context with LLM

This pipeline shows how a language model uses extra information found by searching to give better answers. It first finds helpful text, then mixes it with the question, and finally the model learns to give improved replies.

Data Flow - 4 Stages

1Input Question

1 question string→User asks a question to the system→1 question string

"What is the capital of France?"

↓

2Context Retrieval

1 question string→Search external documents or database to find relevant text→1 question string + retrieved context text

"Paris is the capital city of France."

↓

3Context Integration

1 question string + retrieved context text→Combine question and context into one input for the language model→1 combined input string

"Question: What is the capital of France? Context: Paris is the capital city of France."

↓

4Language Model Processing

1 combined input string→The language model processes the combined input to generate an answer→1 answer string

"The capital of France is Paris."

Training Trace - Epoch by Epoch

Loss
1.2 |*       
1.0 | *      
0.8 |  *     
0.6 |   *    
0.4 |    *   
0.2 |     *  
0.0 +--------
      1 2 3 4 5
       Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Model starts learning to use context but predictions are rough.
2	0.9	0.60	Model improves understanding of context relevance.
3	0.7	0.75	Better integration of question and context seen.
4	0.5	0.85	Model confidently uses retrieved context to answer.
5	0.4	0.90	Training converges with high accuracy and low loss.

Prediction Trace - 4 Layers

Layer 1: Input Question

Layer 2: Context Retrieval

Layer 3: Context Integration

Layer 4: Language Model Processing

Model Quiz - 3 Questions

What is the main purpose of retrieving context before using the language model?

A To replace the language model completely

B To provide extra information that helps the model answer better

C To make the input shorter

D To confuse the model with more data

Key Insight

Combining retrieved context with a language model helps the model give more accurate and relevant answers by providing it with useful background information. Training shows steady improvement as the model learns to use this extra context effectively.