Comment by Farid Parvez on "The Hidden Mental Model That Determines Whether AI Tools Help You "

I 've completed all 41 videos of the 2022 ICT Mentorship series in YouTube and prepared my own chart annotated NOTES from the 41 episodes.

My objective is - building a knowledge base (KB) of the 2022 ICT Mentorship model, that, later on, can eventually be optimized for Agentic AI, RAG systems, and realistic trading applications.

For that, given the NOTES and attach the annotated chart drawing, as input and meaningful Prompt to the ChatGPT.

ChatGPT gives the output WHICH I THINK IS --

Execution-Grade Trade Logic System (ETLS) → Deterministic → Structured → Machine-readable → Execution-ready

For the 41 videos, such conversations of my input and ChatGPT output counts to around 80.

From all these 80 outputs, I'm attempting to construct - A KB structured as an Execution-Grade, RAG-Optimized Trade Knowledge Architecture, from all these 80 outputs.

where:

ICT concepts are represented with precise semantics.
Trade models are decomposed into modular logic units.
Retrieval can locate the correct logic under the correct market conditions.
Future Agentic AI can reason over the logic.
Future systems can map chart observations to executable ICT models.

Given the Objective, could you please recommend some course on building KB or any organisation offering such service --

Knowledge Engineering
Taxonomy
Ontology Design
Decision Architecture
Rule Systems
Retrieval Architecture

Need your guidance in my endeavor of building the KB that, later on, can eventually be optimized for Agentic AI, RAG systems, and realistic trading applications.

Regards , Farid Parvez, B.E. ( Civil Engineering ) India

Farid, honestly you don't need all six of those disciplines to start. For what you're building, it mostly comes down to one thing: knowledge graphs plus RAG. That's the combination that lets an AI find the right piece of your logic under the right market conditions, which is exactly what you described wanting.

The single most useful resource for your goal is DeepLearning.AI and Neo4j's short course "Knowledge Graphs for RAG" (on Coursera). It's practical and teaches precisely the retrieval problem you named: storing your ICT concepts with their relationships, not just as loose text, so the system pulls the correct model when the conditions match. Class Central also lists several free RAG courses (LangChain, LlamaIndex, DeepLearning.AI) if you want more.

One honest note. The hardest part won't be storing or retrieving the knowledge. It will be the rules that connect a chart observation to a specific trade model: "when I see X on the chart, apply model Y." ChatGPT's "execution-grade" output tends to make that step sound more solved than it is. Get that mapping right and the rest follows.

Happy to point you further if you get stuck there, it's the kind of thing I work on.

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