Your LLM has 128K tokens. Your document has 150K words. Something has to give. What do you do? A)...
LLM con 128K token vs documenti 150K+ parole: chunking, sliding window, progressive summary, o truncation. Una fallisce silenziosamente. Su contratti legali, l'errore emerge in produzione: manager tech devono scegliere la strategia di retrieval corretta prima del deployment.
Your LLM has 128K tokens.
Your document has 150K words.
Something has to give. What do you do?
A) Chunk the document into fixed-size pieces and embed each one — retrieve the top-k at query time.
B) Use a sliding window — process the document in overlapping chunks, stitch the outputs together.
A few months ago, I was building a system to answer questions from 100-page technical PDFs. You know...
Every RAG app and agent eventually hits the same wall: you have more stuff than fits in the model's...
Most discussions about LLM performance focus on the model architecture and prompting. But there's a hidden factor: the tokenizer.…
In the context of building LLM-related applications, chunking is the process of breaking down large pieces of text into smaller…
LLM costs accumulate in ways that are not always obvious. Tokens consumed by system prompts, repeated context windows, and…
If you're building AI agents or running LLM pipelines in production, you already know the pain: tool...
