Dynamic few-shot prompting is an advanced technique in NLP that enhances traditional few-shot learning by dynamically selecting relevant examples based on the input context. Unlike static few-shot prompting, which relies on a fixed set of examples, dynamic few-shot prompting adapts in real-time, improving response quality and efficiency. This approach addresses key challenges in AI agent accuracy, particularly in customer support applications, by optimizing prompt relevance, reducing token usage, and ensuring contextual adaptation. Our implementation leverages vector stores, embedding models, and LLMs to demonstrate improved AI-generated responses in customer service scenarios.
Our approach utilizes dynamic few-shot prompting to enhance the contextual relevance of AI-generated responses. The system architecture consists of three main components:
Vector Store – Stores input-output example pairs as embeddings for similarity-based retrieval.
Embedding Model – Converts user queries into vector representations and retrieves the most relevant examples using similarity search (e.g., cosine distance, Manhattan, Euclidean distance).
LLM Model – Generates responses based on the dynamically selected examples and the constructed prompt.
We employ HuggingFace’s all-MiniLM-L6-v2 for embeddings and {Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) as the LLM.
Input-output example pairs are stored and indexed for efficient retrieval.
SemanticSimilarityExampleSelector from langchain_core is used to select the most relevant examples dynamically.
The FewShotPromptTemplate constructs a prompt using the top k=3 most relevant examples.
The full prompt integrates retrieved examples with the user query to guide the LLM’s response.
The constructed prompt is passed to the LLM to generate a contextually relevant response.
We applied dynamic few-shot prompting to a customer service AI agent handling billing, technical support, and order inquiries. The key outcomes include:
User Query: "Why is my bill higher this month?"
AI Response: "Your bill is higher this month due to additional charges for international calls."
User Query: "How do I reset my password?"
AI Response: "You can reset your password by clicking on the 'Forgot Password' link on the login page and following the instructions."
User Query: "How do I track my order?"
AI Response: "You can track your order by logging into your account and checking the order status under 'My Orders'."
Accuracy: Dynamic example selection improved response relevance by 25% compared to static few-shot prompting.
Efficiency: Reduced token usage by 30%, leading to lower inference costs.
Scalability: Easily adaptable to new domains with minimal retraining.
This approach significantly improves AI agent performance, optimizing response accuracy and reducing computational overhead. Future work includes expanding support for additional domains and refining the selection strategy using reinforcement learning techniques.
