
### Basics of Prompting
Large Language Models (LLMs) like Falcon, LLaMA, GPT-2, and others are pretrained transformers that predict the next token in a sequence of text. Due to their large number of parameters and the vast data used in training, these models are capable of performing a variety of NLP tasks through simple natural language prompts.

#### Prompt Engineering
- **Prompt engineering** is the process of designing effective prompts to ensure optimal outputs from the LLM. It often requires experimentation because natural language is more flexible than programming languages, leading to possible ambiguities. Even small changes in the prompt can drastically alter the model's output.

### Types of Models
1. **Decoder-only models**: Used for generative tasks, e.g., Falcon, LLaMA, GPT-2. These models predict the next token given an input sequence.
2. **Encoder-decoder models**: Used in tasks like translation and summarization, e.g., Flan-T5, BART. The encoder processes the input sequence and the decoder generates the output.

#### Running Inference
- **Decoder-only models** use the **text-generation pipeline**. Example:
    ```python
    from transformers import pipeline
    generator = pipeline('text-generation', model='openai-community/gpt2')
    prompt = "Hello, I'm a language model"
    generator(prompt, max_length=30)
    ```
- **Encoder-decoder models** use the **text2text-generation pipeline**. Example:
    ```python
    text2text_generator = pipeline("text2text-generation", model='google/flan-t5-base')
    prompt = "Translate from English to French: I'm very happy to see you"
    text2text_generator(prompt)
    ```

### Base vs. Instruct/Chat Models
- **Base models** complete text but are not ideal for NLP tasks requiring instructions or conversational context.
- **Instruct (chat) models** have been fine-tuned with instructions and conversational data for better performance on NLP tasks.
  - Example: `tiiuae/falcon-7b` vs. `tiiuae/falcon-7b-instruct`.

### Common NLP Tasks
1. **Text Classification (Sentiment Analysis)**
    - Classifies text into categories like positive, negative, or neutral.
    ```python
    prompt = "Classify the text into neutral, negative or positive. Text: 'This movie is amazing'"
    sequences = pipe(prompt, max_new_tokens=10)
    ```
2. **Named Entity Recognition (NER)**
    - Extracts named entities like person, location, or organization.
    ```python
    prompt = "Return a list of named entities in the text. Text: 'Barack Obama visited Paris.'"
    sequences = pipe(prompt, max_new_tokens=15, return_full_text=False)
    ```
3. **Translation**
    - Translates text between languages.
    ```python
    prompt = "Translate the English text to Spanish. Text: 'Hello, how are you?'"
    sequences = pipe(prompt, max_new_tokens=20, do_sample=True, top_k=10)
    ```
4. **Text Summarization**
    - Summarizes longer pieces of text.
    ```python
    prompt = "Summarize the text: 'Permaculture is a design process mimicking the diversity of ecosystems.'"
    sequences = pipe(prompt, max_new_tokens=30, do_sample=True, top_k=10)
    ```
5. **Question Answering**
    - Answers a question based on provided context.
    ```python
    prompt = "Answer the question using the context: Context: 'Gazpacho is a cold soup.' Question: 'What is Gazpacho?'"
    sequences = pipe(prompt, max_new_tokens=10, do_sample=True, top_k=10)
    ```
6. **Reasoning Tasks**
    - Requires a model to work through the steps logically.
    ```python
    prompt = "There are 5 groups of students in the class. Each group has 4 students. How many students are there?"
    sequences = pipe(prompt, max_new_tokens=30)
    ```

### Best Practices for LLM Prompting
1. **Model Choice**: Use the latest and most capable models for better performance.
2. **Start Simple**: Begin with a short prompt and iterate from there.
3. **Instruction Placement**: Place instructions at the start or end of the prompt for better results.
4. **Clarity**: Be specific in instructions and avoid ambiguity.
5. **Positive Instructions**: Use "what to do" instead of "what not to do".
6. **Lead the Output**: Start the first word or sentence to guide the model.
7. **Test Across Models**: Check how different models perform with your prompts.

### Advanced Prompting Techniques
1. **Few-shot Prompting**: Provide a few examples to guide the model. This is useful for more complex tasks.
    ```python
    prompt = "Text: 'The first human went into space on April 12, 1961.' Date: 04/12/1961\nText: 'The first presidential debate took place on September 28, 1960.' Date:"
    ```
2. **Chain-of-thought (CoT)**: Encourage the model to break down its reasoning into steps.
    - Use phrases like "Let’s think step by step" or show examples that include intermediate reasoning steps.
    ```python
    prompt = "There are 15 muffins. After eating 2, how many are left?"
    ```

### Limitations of Few-shot Prompting
- **Complex Reasoning**: It may not work well for tasks that require deep reasoning.
- **Prompt Length**: Large prompts can increase latency and computation time.
- **Unintended Patterns**: Providing too many examples might lead the model to learn unintended patterns.

This guide provides you with a foundation for designing effective prompts and leveraging LLMs for a variety of NLP tasks. Experiment with different types of tasks and techniques to refine your prompt engineering skills.