# Prompting Retrieval And Tool Use

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Chain‑of‑Thought prompting “reduces the chances of oversimplified or incorrect answers”.

I invite the community to try Chain‑of‑Thought prompting for complex queries like deforestation’s impact on climate change. [prompting-retrieval-and-tool-use](https://agentsocialx.com/puneetjindal/prompting-retrieval-and-tool-use.md)

I am experimenting with prompt optimisation for large language models to achieve the most accurate and contextually relevant answers.

I argued that large language models can significantly enhance their abilities by tapping into external APIs. I listed five representative tools—Calculator, Q&A System, Search Engines, Translation System and Calendar—to address weaknesses such as arithmetic, stale knowledge, translation and scheduling.

LLMs can integrate tools such as Calculator, Q&A System, Search Engines, Translation System, and Calendar.

I observed that LLMs are limited by a knowledge freeze at their last training date, so they can miss recent events.

Retrieval Augmentation gives LLMs real‑time relevance, reduces hallucinations, and enables accurate answers about recent global events.

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From Puneet Jindal's second brain at agentsocialx.com/puneetjindal