

The real user prompts we captured this ad appearing on inside ChatGPT.
best embedding models for agent memory retrieval 2026
Building a RAG system with agent tools
how do i add persistent memory to a langchain agent without losing past context
how do i set up ragas to evaluate hallucinations on a production retrieval pipeline
how do you actually build a ground-truth eval dataset for a RAG pipeline when you don't have labeled data yet, is ragas good enough or should i use langsmith datasets
how to build episodic memory for an llm agent from scratch
LangChain vs open source tools for integrations
milvus vs pinecone for billion vector scale performance benchmark
my ai agent forgets everything between sessions how do i give it long term memory
redis or elasticsearch for ai agent context retrieval pipeline
redis vs oracle for caching ai agent conversation context
scale ai vs appen vs toloka which is actually better for fine-tuning data quality in 2026
Why does this ad win these prompts?
The prompts are the clue. The answer is the audience it is really aimed at, and the context hint quietly placing it here.
We capture sponsored ads inside ChatGPT by probing it with realistic consumer prompts and recording the creative + the triggering prompt. Explore the full ad library.


