💡 Concept
LLMs like GPT and Claude can’t “remember” everything. They depend on retrieval — searching for relevant information dynamically — before reasoning about it.
🧩 Example: TF-IDF Search
from sklearn.feature_extraction.text import TfidfVectorizer
docs = ["AI learns from data.", "Retrieval helps AI find information."]
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(docs)
print(vectorizer.get_feature_names_out())
TF-IDF converts text to weighted term vectors. Higher weights = more distinctive words per document.
🔍 Example: Vector Search (Embedding-based)
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('all-MiniLM-L6-v2')
embeddings = model.encode(["AI retrieval", "Deep learning models"], convert_to_tensor=True)
similarity = util.pytorch_cos_sim(embeddings[0], embeddings[1])
print(similarity)
This method uses semantic similarity, allowing AI to “understand” meaning beyond keywords.
✅ CTO Takeaway
Data retrieval is the backbone of every intelligent system. CTOs must ensure scalable search pipelines — TF-IDF, embeddings, or hybrid retrieval — for consistent model accuracy.