Retrieval
The process of finding and fetching relevant information from a database or knowledge base in response to a query.
In-Depth Explanation
Retrieval in AI systems is the process of finding and returning relevant information from a knowledge store in response to a query. It's the "R" in RAG and a critical component of accurate AI systems.
Retrieval approaches:
- Keyword search: Traditional TF-IDF or BM25 matching
- Semantic search: Using embeddings to find conceptually similar content
- Hybrid search: Combining keyword and semantic approaches
- Knowledge graph queries: Traversing structured relationships
Retrieval pipeline stages:
- Query processing: Clean and prepare the search query
- Candidate selection: Find potentially relevant items efficiently
- Ranking: Order candidates by relevance
- Filtering: Apply business rules and metadata filters
- Re-ranking: Optionally use an LLM to refine rankings
Key retrieval metrics:
- Precision: Fraction of retrieved items that are relevant
- Recall: Fraction of relevant items that are retrieved
- MRR: How high the first relevant result appears
- nDCG: Quality of ranking considering position
Effective retrieval is often the difference between a good and bad RAG system - poor retrieval produces poor answers regardless of how good your LLM is.
Business Context
Effective retrieval is the foundation of accurate AI. Poor retrieval means poor answers, regardless of how good your LLM is.
How Clever Ops Uses This
We invest heavily in retrieval optimisation for our Australian clients. Sophisticated retrieval strategies - hybrid search, re-ranking, metadata filtering - often matter more than model choice.
Example Use Case
"Retrieving the 5 most relevant document chunks to answer a customer question about your product return policy."