You can improve the accuracy of responses from LLMs by providing context in your prompts.
The context could include relevant information, data, or details that help the model generate more accurate and relevant responses.
Avoiding hallucination
Providing context can help minimize hallucinations by anchoring the model’s response to the facts and details you supply.
For example, if you ask a model to summarize a companies performance, the model is more likely to produce an accurate summary if you include a relevant stock market report in your prompt.
Access to data
LLMs have a fixed knowledge cutoff and cannot access real-time or proprietary data unless it is provided in the prompt.
If you need the model to answer questions about recent events or organization-specific information, you must supply that data as part of your prompt. This ensures that the model’s responses are up-to-date and relevant to your particular use case.
You could also provide statistics or data points in the prompt to help the model include useful facts in its response.
Supplying context
Supplying context in your prompts helps LLMs generate more accurate, relevant, and trustworthy responses by reducing hallucinations and compensating for their lack of access to external or current data.
Check Your Understanding
Providing Context to LLMs
Which of the following statements are true about providing context to Large Language Models (LLMs)? (Select all that apply)
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✓ Providing context in a prompt can help reduce hallucinations in LLM responses.
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✓ Supplying up-to-date information in a prompt allows the LLM to generate more relevant answers.
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✓ Including specific instructions or examples in a prompt can improve the accuracy of the model’s output.
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❏ LLMs can access real-time data from the internet without any context provided in the prompt.
Hint
LLMs provide responses based on their training data and the context given in the prompt.
Solution
The following statements are true:
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Providing context in a prompt can help reduce hallucinations in LLM responses.
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Supplying up-to-date information in a prompt allows the LLM to generate more relevant answers.
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Including specific instructions or examples in a prompt can improve the accuracy of the model’s
LLMs cannot access real-time data.
Lesson Summary
In this lesson, you learned about how providing context in your prompts can help reduce hallucinations and improve the accuracy of LLM responses.
In the next module, you will learn about you can use RAG (Retrieval-Augmented Generation) to include additional context in your prompts.