more examples

guides/prompts-basic-usage.md

@@ -23,7 +23,7 @@ Output
 Antibiotics are a type of medication used to treat bacterial infections. They work by either killing the bacteria or preventing them from reproducing, allowing the body’s immune system to fight off the infection. Antibiotics are usually taken orally in the form of pills, capsules, or liquid solutions, or sometimes administered intravenously. They are not effective against viral infections, and using them inappropriately can lead to antibiotic resistance.
 ```
 
-Let's just assume that this is too much information and want to summarize it further. In fact, we can instruct the model to summarize to one sentence like so:
+The "A:" is an explicit prompt format that's used in question answering. I used it here to tell the model that there is an expected further. In this example, it's not clear how this is useful vs not using it but we will leave it that for later examples. Let's just assume that this is too much information and want to summarize it further. In fact, we can instruct the model to summarize into one sentence like so:
 
 ```
 Antibiotics are a type of medication used to treat bacterial infections. They work by either killing the bacteria or preventing them from reproducing, allowing the body’s immune system to fight off the infection. Antibiotics are usually taken orally in the form of pills, capsules, or liquid solutions, or sometimes administered intravenously. They are not effective against viral infections, and using them inappropriately can lead to antibiotic resistance.
@@ -36,13 +36,90 @@ Output
 Antibiotics are medications used to treat bacterial infections by either killing the bacteria or stopping them from reproducing, but they are not effective against viruses and overuse can lead to antibiotic resistance.
 ```
 
-Without paying too much attention to the accuracy of the output above, which is something we will touch on in a later guide, the model tried to summarize the paragraph in one sentence. You can get clever with the instructions but we will leave that for the learner to practice more. 
+Without paying too much attention to the accuracy of the output above, which is something we will touch on in a later guide, the model tried to summarize the paragraph in one sentence. You can get clever with the instructions but we will leave that for a later chapter. Feel free to pause here an experiment to see if you get better results.
 
 ## Information Extraction
-...
+While language models are trained to perform natural language generation and related tasks, it's also very capable of performing classification and a range of other natural language processing (NLP) tasks. 
+
+Here is an example of a prompt that extracts information from a given paragraph.
+
+```
+Author-contribution statements and acknowledgements in research papers should state clearly and specifically whether, and to what extent, the authors used AI technologies such as ChatGPT in the preparation of their manuscript and analysis. They should also indicate which LLMs were used. This will alert editors and reviewers to scrutinize manuscripts more carefully for potential biases, inaccuracies and improper source crediting. Likewise, scientific journals should be transparent about their use of LLMs, for example when selecting submitted manuscripts.
+
+Mention the large language model based product mentioned in the paragraph above:
+
+The large language model based product mentioned in the paragraph above is ChatGPT.
+
+```
+
+Output
+```
+The large language model based product mentioned in the paragraph above is ChatGPT.
+```
+
+There are many ways we can improve the results above, but this is already very useful. 
+
+By now it should be obvious that you can ask the model to perform different tasks by simply instructing it what to do. That's a powerful capability that AI product builder are already using to build powerful products and experiences.
+
+## Text Classification
+So far, we have used simple instructions to perform a task. As a prompt engineer, you will need to get better at providing better instructions. But that's not all! You will also find that for harder use cases, just providing instructions won't be enough. This is where you need to think more about the context and the different elements you can use in a prompt. Other elements you can provide are `input data` or `examples`. 
+
+Let's try to demonstrate this by providing an example of text classification.
+
+```
+Classify the text into neutral, negative or positive. 
+
+Text: I think the food was okay. 
+Sentiment:
+```
+
+Output
+```
+Neutral
+```
+
+We gave the instruction to classify the text and the model responded with `'Neutral'` which is correct. Nothing is wrong with this but let's say that what we really need is for the model to give the label in the exact format we want. So instead of `Neutral` we want it to return `neutral`. How do we achieve this. There are different ways to do this. We care about specificity here, so the more information we can provide the prompt the better results. We can try providing examples to specific the correct behavior. Let's try again:
+
+```
+Classify the text into neutral, negative or positive. 
+
+Text: I think the vacation is okay.
+Sentiment: neutral 
+
+Text: I think the food was okay. 
+Sentiment:
+```
+
+Output
+```
+neutral
+```
+
+Perfect! This time the model returned `neutral` which is the specific label I was looking for. To highlight why sometimes being specific is important, checkout this example and spot the problem:
+
+```
+Classify the text into nutral, negative or positive. 
+
+Text: I think the vacation is okay.
+Sentiment:
+```
+
+Output
+```
+Neutral
+```
+
+What is the problem here?
+
+
+Paragraph source: [ChatGPT: five priorities for research](https://www.nature.com/articles/d41586-023-00288-7) 
 
 ## Role-Playing
 ...
 
 ## Reasoning
+...
+
+
+## Code Generation
 ...