AI Prompt Engineering · Lesson

Dynamic Few-Shot Selection

Retrieving examples per query.

From Static to Dynamic Demos

Static few-shot uses the same examples for every query. Dynamic few-shot retrieves the most relevant demonstrations per query from a pool, conditioning the model on examples that resemble the current input.

This is retrieval-augmented in-context learning: it raises the relevance of demonstrations, which is one of the strongest levers for ICL quality, especially on heterogeneous traffic.

class DynamicSelector:
    def __init__(self, pool, embedder, index):
        self.pool = pool          # candidate demonstrations
        self.embed = embedder
        self.index = index        # ANN index over pool embeddings
    def select(self, query, k):
        q = self.embed(query)
        ids = self.index.search(q, k)
        return [self.pool[i] for i in ids]

Embedding the Demonstration Pool

Precompute embeddings for every candidate demonstration and store them in an approximate nearest neighbor index (FAISS, HNSW, or a vector DB). At query time, embed the input once and retrieve the top matches.

Choose an embedding model aligned with your task semantics; a generic embedder may cluster on surface features rather than the dimension that predicts the correct label.

import numpy as np

def build_index(pool, embed):
    vecs = np.stack([embed(d.input) for d in pool]).astype('float32')
    vecs /= np.linalg.norm(vecs, axis=1, keepdims=True)  # cosine via dot
    index = HNSW(dim=vecs.shape[1])
    index.add(vecs)
    return index

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