Prompt injection for long-context LLMs as an alternative to RAG?

FOOTNOTE:

I was able to rerun the above test with GPT4o (128k context) , making sure to use large token / chunk settings… but it’s still very flaky for my white paper Q/A use case… (lost in the middle, lost at the end , etc.) …here’s my settings if anyone wants to duplicate and refine. .Would love it if we can find the right settings for this case :

CUSTOM AI PERSONA
Enabled? Yes
Priority Yes
Allow Chat Yes
Allow Mentions Yes
Vision Enabled No
Name Rag Testing Bot 3
Description Test RAG vs Long Context prompt injection
Default Language Model GPT-4o-custom
User Rag_Testing_Bot_bot
Enabled Commands Categories, Read, Summary
Allowed Groups trust_level_4
System Prompt Answer as comprehensively as possible from the provided context on Equatic Carbon Removal Research in the attached file. Do not invent content. Do not use content external to this session. Focus on content provided and create answers from it as accurately and completely as possible.
Max Context Posts 50
Temperature 0.1
Top P 1
Uploads Equatics-paper1-with-unique-haystack-needles-v116.txt
Upload Chunk Tokens 1024
Upload Chunk Overlap Tokens 10
Search Conversation Chunks 10
Language Model for Question Consolidator GPT-4o-custom
CUSTOM BOT
Name to display GPT-4o-custom
Model name gpt-4o
Service hosting the model OpenAI
URL of the service hosting the model https://api.openai.com/v1/chat/completions
API Key of the service hosting the model D20230943sdf_fake_Qqxo2exWa91
Tokenizer OpenAITokenizer
Number of tokens for the prompt 30000