Text to Text Prompt

# Techniques for using text prompts effectively:

## TASK SPECIFICATION-
Text prompts should explicitly specify the objective to the LLM to ​increase accurate responses. ​For example, the prompt translate this English sentence into French.

## CONTEXTUAL GUIDANCE-
which text prompts provide ​specific instructions to the LLMs to generate relevant output.
​For example, if you'd like the model to generate a short write up on the landmarks ​of New York City, a prompt like write a short paragraph on New York City would ​yield a generic response
that might not cover what you want. 
​On the other hand, a more specific prompt like write a short paragraph on ​New York City, highlighting its iconic landmarks, would generate a more ​appropriate output because of the context included in the prompt.

## DOMAIN EXPERTISE-
text prompt using domain specific terms in specialized feilds for accuracy and precision
ex-
like medicine, law, or engineering
Ex-
you'd like to request medical information regarding hypothyroidism. 
​Your prompt could read something like, please explain the causes, symptoms, and ​treatments of hypothyroidism, ​including the latest research and medical guidelines. 

## BIAS MITIGATION-
technique in which text prompts provide explicit instructions ​to generate neutral responses.
​For instance, let's assume that you are concerned about a gender bias in ​the model's response to a prompt for a writeup on leadership qualities.
To address this, you can use a text prompt like this, ​write a 100-word paragraph on leadership traits    without favoring any gender. ​Provide equal examples of traits from all genders

## FRAMING-
text prompts guide LLMs to generate ​responses within required boundaries
ex-
you'd like the model to summarize a lengthy article about climate change. ​Your text prompt can be, provide a summary of 100 words of the article on climate ​change, focusing on its primary findings and recommendations. ​

## ZEROSHOT-PROMPTING
method using which generative AI models generate ​meaningful responses to prompts without needing prior training on these specific ​prompts.

giving the model a clear instruction or question without any prior examples or training on that specific task, and the model can still respond correctly.
ex-
​For example, the prompt could be, select the adjective in this sentence. ​The sentence is Anita bakes the best cakes in the neighborhood. ​The output here would be best. ​

## FEEDBACK LOOP-
used  cuz we may not get desired o/p in one prompt 
therefore we need to iterate  the process.
therefore feedback loop techq comes into picture.


​wherein users provide feedback to text prompts and ​iteratively refine them based on the response generated by the LLM. ​This loop allows users to improve the model's output quality incrementally, ​until the desired state is achieved. 

​For example, the user asks the model to write a poem via a text prompt, ​the LLM generates a poem.
The user says, make it more humorous. ​The LLM adjusts the poem to include more humorous elements. ​The user approves the revised poem. 

## FEW SHOOT-
for complex tasks, when you are unable to describe your needs clearly fewshoot techq used.

​It enables in context learning wherein demonstrations are provided in the prompt ​to steer the model to better performance. ​The demonstrations act as conditioning for ​subsequent examples where you would like the model to generate a response. ​For instance, suppose the task for ​the model is to generate short travel recommendations. 

"Recommend a summer travel destination well known for beautiful beaches." ​"Suggest a fall travel destination that is renowned for its beautiful foliage." 
​After using these few-shot prompts, the model can generate travel recommendations ​for other types of vacations.
 example, if the task is "recommend a city to explore." ​The model will generate an answer- "consider visiting a vibrant city like ​Paris, known for its rich history, art, and iconic landmarks." 
 ​This is how the model can generate travel recommendations for different types of ​vacations, 
 based on the minimal training data provided in the few-shot prompts. 


ex- Customer Support Chatbot for an E-commerce Company.
The prompt includes example questions and answers to guide the model on how to respond effectively.