Moderate Cost Question 124 of 223

How do you reduce token cost in a production RAG app?

GenAI / LLM · Speak this in 60–90 seconds · Faridabad & Delhi NCR

The model does not read letters like humans. It reads these pieces, then predicts the next one.

Tighten prompts, cap history, retrieve fewer better chunks, and cache embeddings and repeated prefixes.

LLMs use tokens (not full words) because it helps them:

This is a process

question about Cost.

Panels listen for order,

trade-offs, and what you would actually do on a GenAI / LLM project - not buzzwords.

Stable token IDs

Each piece maps to a number the network can learn.

Fits the model

Fixed pieces are what transformers expect as input.

Before the model can read a sentence, it goes through these steps:

1
Tighten prompts, cap history,
retrieve fewer better chunks, and cache embeddings and repeated prefixes.
2
Route simple intents to
a small model.
3
Measure cost per successful
task, not only tokens per call.
4
Context mix
Attention looks at nearby tokens together.
5
Next token
The model scores what should come next.
6
Decode
IDs turn back into readable text.

Let's see how a real sentence is tokenized (tokens may vary by model):

Input text

“Route simple intents to a small model.”

Tokenized output

Routesimpleintentstoasmall

Token IDs (example)

2987408337471632900

Note: Actual tokens and IDs depend on the tokenizer (e.g., GPT, Llama, etc.).

Tighten prompts, cap history, retrieve fewer better chunks, and cache embeddings and repeated prefixes. Route simple intents to a small model.