Uploaded November 2025 | Updated September 2026, 2 hours ago
Why do LLMs hallucinate and how can we fix it? đđŻ
Even the best models can produce wrong answers when:
đ Context is missing
đ Prompts are unclear
đ Too much irrelevant data is fed in
In this video, I cover how to minimize hallucinations in real-world AI projects - from writing better prompts to structuring cleaner data, optimizing context windows, and building evaluation loops that actually catch errors before deployment.
Key Highlights:
- Start with clear, positively phrased prompts
- Connect models to real data (RAG)
- Clean and structure your data for reliable retrieval
- Verify with proper evaluations - DO YOUR EVALS!
- Fine-tune only when truly needed
- Scale with advanced RAG and RLFT for better performance
Hallucinations wonât disappear completely but with the right systems, they can shrink dramatically.
đ Follow Me for more such Content.
#ai #llms #grounding #short
Why do LLMs hallucinate and how can we fix it? đđŻ
Even the best models can produce wrong answers when:
đ Context is missing
đ Prompts are unclear
đ Too much irrelevant data is fed in
In this video, I cover how to minimize hallucinations in real-world AI projects - from writing better prompts to structuring cleaner data, optimizing context windows, and building evaluation loops that actually catch errors before deployment.
Key Highlights:
- Start with clear, positively phrased prompts
- Connect models to real data (RAG)
- Clean and structure your data for reliable retrieval
- Verify with proper evaluations - DO YOUR EVALS!
- Fine-tune only when truly needed
- Scale with advanced RAG and RLFT for better performance
Hallucinations wonât disappear completely but with the right systems, they can shrink dramatically.
đ Follow Me for more such Content.
#ai #llms #grounding #short
