How to Reduce LLM Token Costs Without Losing Answer Quality

Why Token Costs Are High in AI Applications

Most AI applications built on RAG are paying for noise. The core problem: RAG retrieves text chunks that contain the relevant signal surrounded by irrelevant context — and sends all of it to the LLM.

The retrieval tax

A RAG system answers a question about a drug's formulary tier by retrieving several document chunks from its vector database. Each chunk might be 400–600 tokens. The relevant fact — "Tier 2 coverage for Type 2 diabetes" — might be 15 tokens. The other 585 tokens are tax: surrounding text the model reads, processes, and pays for, even though it contributes nothing to the answer.

The three token waste patterns

• Context stuffing. Teams increase the number of retrieved chunks hoping to capture the right answer somewhere in the context. More chunks = more tokens = more cost, with no guarantee of accuracy improvement. RAG at 2,982 tokens per query is context stuffing as the default operating mode.
• Redundant retrieval. The same domain knowledge — the same formulary structure, the same payer hierarchy, the same regulatory taxonomy — gets re-retrieved on every query. There is no memory between queries. Every call re-pays for the same domain facts.
• Unstructured chunks. Raw text requires the model to infer entity relationships from prose. Encoding "Drug A is covered at Tier 2 under Plan B for Indication C" in prose takes 30–60 tokens. The same fact in a CKG dependency row takes 6. Structure is inherently token-efficient; prose is not.

The Math: 11× Token Reduction Per Query at Scale

The numbers are from a fully reproducible benchmark — not marketing estimates. 45 domains, 7,928 queries, every result verifiable.

Source: Yarmoluk & McCreary, "Compact Knowledge Graphs vs. RAG and GraphRAG: A Reproducible Benchmark Across 45 Educational Domains," arXiv 2026. Full benchmark on GitHub →

Why Compressing Tokens Doesn't Mean Compressing Quality

The counterintuitive result: CKG uses 11× fewer tokens and achieves 3.8× better accuracy. This seems contradictory until you understand what the extra RAG tokens actually contain.

RDS: 42× more intelligence per token

Retrieval Density Score (RDS) measures correct information delivered per token spent. CKG RDS = 0.001751. RAG RDS = 0.0000413. The 42× advantage means each CKG token carries 42× more factually correct information than a RAG token.

RDS = F1 Score / Mean Tokens Used

CKG:  0.4709 / 269  = 0.001751
RAG:  0.1231 / 2982 = 0.0000413

Advantage: CKG delivers 42× more correct information per token

The token composition difference

A RAG token is on average: ~5% signal (the relevant fact), ~60% context (surrounding prose that supports the fact), and ~35% noise (retrieved text that is not relevant to this query). CKG eliminates the noise and compresses the context — the signal-to-token ratio is structurally higher.

Fewer tokens does not mean less information when the removed tokens were noise. It means a higher density of correct information in the remaining tokens.

How CKG Delivers the Same Domain Knowledge in 269 Tokens vs. 2,982

A CKG encodes domain knowledge as explicit entity relationships, not prose. The difference in representation efficiency is structural, not a compression trick.

RAG representation: prose with embedded facts

A RAG system stores facts in sentences: "Ozempic (semaglutide) is a GLP-1 receptor agonist indicated for Type 2 diabetes management. Under the BlueCross Medicare Advantage formulary, Ozempic is covered at Tier 2 with prior authorization required for new starts..." At 40–60 tokens per fact, encoding 50 relationships requires 2,000–3,000 tokens of prose.

CKG representation: structured dependency rows

The same knowledge in CKG format:

ConceptID,ConceptLabel,Dependencies,TaxonomyID
1,GLP-1 Receptor Agonist,,FOUND
2,Semaglutide,1,CORE
3,Ozempic (Brand),2,CORE
4,Medicare Advantage Plan,1,CORE
5,Tier 2 Formulary Coverage,3|4,ADV
6,Type 2 Diabetes Indication,2,CORE
7,Prior Authorization Required,5|6,ADV
...

Each row averages 6–8 tokens. Fifty relationships = ~350 tokens including the header. The relationship between any two entities is expressed as a dependency ID — one token. RAG expresses the same relationship in a sentence — 15–40 tokens. Structure is the mechanism of compression.

Cost Comparison: $13.53 vs. $72.58 for the Same 7,928-Query Benchmark

The benchmark cost figures are actual API costs from running identical queries against both systems. Not projections. Not estimates.

At 10,000 Queries/Month: Real Dollar Math

If your team or product runs 10,000 LLM queries per month against a domain knowledge base, here is what the CKG vs. RAG difference looks like in production dollars.

Fine-Tuning vs. Inference Cost: Where CKG Fits

Teams facing high token costs often ask whether fine-tuning is a better solution than better retrieval. The answer depends on the cost bucket you are trying to reduce.

Fine-tuning reduces inference tokens — but not all of them

Fine-tuning bakes domain knowledge into model weights, reducing the amount of context needed at inference time. But it comes at significant upfront cost (GPU compute, labeled data, retraining cadence), does not generalize to new or updated domain facts, and still requires some retrieval context for current-state queries.

CKG reduces inference tokens without fine-tuning overhead

CKG delivers structured domain knowledge in a 269-token system prompt. No fine-tuning. No GPU cluster. No retraining cadence. Domain updates require swapping the .md file — minutes, not months. For production systems where domain facts change frequently (formularies, regulations, pipelines), CKG is more practical than fine-tuning.

CKG can also improve fine-tuning

For teams already fine-tuning, CKG-derived data is higher-quality training signal than raw text. Structured, relationship-explicit data produces more accurate domain-specific models than prose fine-tuning data. CKG and fine-tuning are complementary, not competing.