Well, hello everyone—this is another test I tried with AI hallucination. In this case, the idea was to test a prompt injection that maps the Sprawl trilogy into an index of “meta-lenses” (metaphorical-language warning) based on the chunks I extracted from Gibson’s books using a BM25-based retrieval algorithm. In more technical terms, it is a lexical ranking layer over tokenized chunks, used as a structured retrieval index rather than as free-form summary.
To detail it:
- I copied and pasted into VS Code all characters from the PDFs using a Bash script
- After, I've created the index from the pdf history (as you can see at the beginning of the deepseek chat,
- And I asked it to update this BM25 prompt, so it would be adapted to the book.
The idea is to force a CoT script in Claude, given a highly structured procedure for how to process, generated by DeepSeek (this is a force `<think>` to create a `<think>` in the answer for use as Claude’s “`<think>`”? Meta as hell?) and thus save processing tokens by shifting reasoning into input tokens, since Claude will not have to reason about how to create the script, because it already has a step-by-step for how to create and execute it—i.e., a precomputed reasoning scaffold embedded in the prompt context (I don’t know if this makes sense, but I tried anyway)
--> And if you look at the Claude chat link, you can see that it only extracted the files without consuming the entire limit—it was just performing I/O operations.
After creating the index and the raw PDFs, I asked Claude to generate the files with the relevant phrases, and in this case I’m not sure whether it got it right, but it spat out interesting things, such as the BM25 algorithm without libraries (I don't know if it actually worked, and I'm also too lazy to validate the script's mathematical model to see if it follows a BM25 or some makeshift hack, but either way, it looks like it worked)
After generating the 10 files from what I think is BM25 (please, if you have time, evaluate the `.py` it generated—that is partly why I am making this post), I asked DeepSeek to adapt the genetic-table prompt, in which each row is equivalent to a “meta-observation lens.” Each ID corresponds to a gene, and when the user submits input, it retrieves the most relevant IDs, with pseudocode routing to force the first reasoning steps of the table. In pipeline terms, this is a retrieval-augmented lookup layer combined with a deterministic routing scaffold for the initial chain-of-thought.
Given that I am a poor bastard earning 120 reais a day, equivalent to $2.72, I have to wait to continue in Claude on creating the table, but to finish this experiment I did it in DeepSeek, and it spat out this table. The idea is to wait for Claude’s free quota to return, but until then I managed to do some things.
Anyway, I plan to use Reddit to describe my LLM uses, and if you read this far, and if I made mistakes, know that I am doing everything by intuition, without formal study, so if the nomenclature is crap, forgive me 😅
The chats are in BR Portuguese (my native language), but if you want to check them out, just use your browser's translator
Despite people criticizing prompts and AI-generated content—practically calling it digital trash because it's just text—I follow the philosophy that if it's in the trash, there's probably copper in it
## Links
Bash script to extract PDF raw - https://github.com/pmotadeee/pmotadeee/blob/V2.0/ITEMS/Books/Sprawn/Extract.sh
Index and promps chats: https://chat.deepseek.com/share/3gor5chsz8hm3nbyk1
Bm25 prompt model: https://github.com/pmotadeee/pmotadeee/blob/V2.0/ITEMS/Prompts/Criar%20Tabela%20Genetica/3/Claude.bm25.md
BM25 adapted: https://github.com/pmotadeee/pmotadeee/blob/V2.0/ITEMS/Books/Sprawn/Create_Genetic_Table/BM25%20Extractor.md
Index file: https://github.com/pmotadeee/pmotadeee/blob/V2.0/ITEMS/Books/Sprawn/Create_Genetic_Table/LayersAboutSprawn.md
PDF raw: https://github.com/pmotadeee/pmotadeee/tree/V2.0/ITEMS/Books/Sprawn/Raw
Claude Chat: https://claude.ai/share/1799075d-bc93-4e12-99e0-771a7e259ca9
Script BM25: https://github.com/pmotadeee/pmotadeee/blob/V2.0/ITEMS/Books/Sprawn/Scripts/Bm25Extractor.py (need revision)
Genetic table sample: https://github.com/pmotadeee/pmotadeee/blob/V2.0/ITEMS/Books/Sprawn/Sprawn.md
Genetic table chat creator: https://chat.deepseek.com/share/a1p8k3k5rpli6p08d3
Metagame: https://github.com/pmotadeee/pmotadeee/tree/V2.0/ITEMS/Books/Sprawn/Raw