AVAILABLE FOR WEB, FLUTTER & QA PROJECTS
How to use this tool
1. Select Average Chunk Token Size
Enter your standard chunk size (e.g. 350 to 512 tokens per passage).
2. Set Top-K Retrieval Depth
Configure how many chunks the vector database returns (typically 3 to 20).
3. Toggle Cross-Encoder Reranker
Optionally enable a reranker (e.g. Cohere Rerank / BGE-reranker) to filter initial top-k to top-n.
4. Review Prompt Token Load & Latency
Inspect total context tokens, estimated reranker latency penalty, and cost per query.
Formula or logic used
Top-K Context Loading & Retrieval Trade-Offs
Higher Top-K values increase recall (finding relevant facts) but linearly inflate prompt token cost, First Token Latency (TTFT), and risk distracting the LLM.
Total Retrieved Context Tokens: $T_{\text{retrieved}} = K \times \text{Avg Chunk Size}$Reranker Filter: Initial retrieval of $K_{\text{initial}}$ (e.g. 25), reranked down to top $K_{\text{final}}$ (e.g. 5)Prompt Token Cost per Query = $(T_{\text{system}} + T_{\text{query}} + T_{\text{retrieved}}) \times P_{\text{in}}$Optimal Balance: $K = 4 \text{ to } 7$ for direct LLM ingestion, or $K = 20 \to 5$ with reranker
Examples
Example 1: Direct RAG Ingestion (No Reranker)
Clean, fast setup ideal for real-time customer support bots requiring immediate answers.
Example 2: Two-Stage Retrieval with Cross-Encoder (K=25 -> 5)
Maximizes retrieval accuracy for complex technical or legal search without flooding LLM context.
Common use cases
RAG Precision vs Recall Balancing
Find the sweet spot between missing an answer (K too low) and confusing the LLM (K too high).
Two-Stage Reranking Architecture
Design high-recall initial vector search paired with Cohere or ColBERT rerankers.
Context Window Cost Management
Prevent oversized Top-K configurations from draining monthly OpenAI API budgets.
Latency Budgeting
Keep end-to-end user query latency under 1.5 seconds by tuning retrieval depth.
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Frequently asked questions
What is Top-K in RAG applications?
Top-K is the number of most similar document chunks retrieved from the vector database based on cosine similarity or dot-product distance to the user's query.
Why not just set Top-K to 50 or 100?
Setting Top-K too high fills prompt context with irrelevant passages ('noise'), increases LLM API costs exponentially, triggers 'Lost in the Middle' hallucinations, and slows down response times.
What is the benefit of using a Reranker with Top-K?
Vector search (bi-encoder) is fast but approximate. A cross-encoder reranker scores the top 20-30 chunks with deep semantic attention and passes only the top 3-5 best matches to the LLM.
How do I choose between Top-K 3, 5, or 10?
Use Top-K = 3 to 5 for concise factual queries (FAQ, definitions). Use Top-K = 7 to 10 for multi-faceted research or comparison queries where facts span multiple chapters.
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