In the world of AI (Artificial Intelligence) we have a lot of Large Language models (LLMs) out there like ChatGPT by OpenAI, BERT by Google, etc. but what if we are not able to use them effectively for our use cases. For getting a precise and more correct output from any of the model actually require a good question or input. But writing a good prompt is also a task which becomes easy when we have the knowledge of Prompt Engineering.
Prompt Engineering is the practice of crafting effective and efficient prompts for Natural Language Processing (NLP) and machine learning models. An effective prompt also boosts the performance and behavior so that the model will generate accurate and context-based responses. We use this technique in various tasks such as text-generation, Translation, summarization, text-to-image generation, etc. Actually, model’s response is totally depending on our prompt or input. If our prompt doesn’t give much information about the task then chances are higher that our model will produce inaccurate responses. So here we are dealing with prompt engineering, strategies and exploring its significances.
Before moving into the concept of prompt engineering let us have a look at some of the basics of LLMs.
LLMs (large language models) are the AI (Artificial Intelligence) models which are pre-trained on massive amount of text data to generate and understand human language this is the reason why these are gaining popularity in the field of NLP (Natural Language Processing). Actually, language models are not new to us these are present on the internet from more than 50 years. But these become large in the year 2017 when the 1st large language model was introduced named as Transformers.
LLMs can perform a wide range of tasks such as summarization, translation, question answering Chatbots, and much more. Some of the examples of LLMs are ChatGPT, BERT, T5, etc.
There are two types of LLM: -
These are the LLMs which are mainly designed for predicting the next word or number or words. These models are not designed to answer questions, have conversation or to solve problems. These models are trained on less amount of data as compare to other LLMs.
For example, if we give a base LLM a sentence like “What are the famous social networks?” then it may respond us with something like “why do people use social networks?” or “what are the benefits of social networks?” as the text given by the base model is not similar to what we expect. To overcome these limitations, Instruction tuned LLMs comes into picture.
Instruction tuned LLMs are those models which are fine-tuned on large amount of data specially for the task of generating and understanding human languages. These models will help you in problem solving, answering questions or conversations tasks. Instruction tuned LLMs are built on top of the Base LLMs.
A Base LLM is taken and further trained on large datasets which also cover some of the examples on how the model will perform on certain instructions. Then the model is fine-tuned with the technique called as “Reinforcement Learning with Human Feedback (RLHF)” which allow the model to improve its performance.
Prompt hacking is used to exploit the vulnerabilities of the LLMs simply by manipulating the input. Prompt hacking could be both ethical as well as unethical on the basis of our uses.
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