Have You ever observed a search engine like when typing something on Google search or a keyboard in your phone suggesting text while you type an incomplete word, these are Nothing but a method called . Now the query arises What’s Language modelling? It’s a method which uses various statistical and probabilistic methods to find out the probability of a given sequence of words occurring in a sentence. In easy words, it is a task of predicting what word should come up next, the following word which can come shall be the word with a high probability.

You’re thinking that of a sentence and provides it to a model like “The rain is _____” then the following word “falling” which got here has the next probability than other words so it got here first as an output.

Now, What are Large Language models? . It guesses the probability of word after word after word, But there may be also a drawback with LLMs is that they hallucinate. With the assistance of multiple prompt engineering techniques, LLMs can respond with factual information without hallucinations which we’ll see on this blog later.

Identical to physics, chemistry and maths has terminologies. Large language models has also a typical language to speak with AI those are called prompts. “. Before jumping into several types of prompts let’s have a have a look at several types of components of prompts that are as follows -:

1. : Instruction Tells the AI model what task must perform like summarising a given text, translating a sentence, classifying an input, or generating a coherent response etc.
2. : Context provides additional information to the LLM, helping it to raised understand the duty and generate more accurate responses
3. : Input data refers to the data that the LLM processes to finish the duty set by the prompt. It will probably be in the shape of text, numbers, or some other structured or unstructured data.
4. : Output indicators are signals provided to the LLM that indicate the model should generate a response. These indicators could be explicit, reminiscent of specifying the format or form of output required, or implicit, where the LLM is anticipated to generate a response based on the given instructions and input data.

1. : Zero-shot prompting enables a model to generate output for tasks it hasn’t been explicitly trained on. It and prompts to perform the duty without specific training data. No input data is required because the model utilizes its inherent knowledge to generate responses.
2. : In one of these Prompt LLMs are presented with a , allowing them to generate or classify recent data just like the provided example.
3. : Few-shot prompting works by presenting the AI model with a of particular tasks or concepts together with prompts and directions. So model uses these examples to generate or classify recent data that is analogous to the examples provided.
4. : There are some advantages of LLMs, they do text summarisation, image generation, code generation, code optimization etc higher than the human brain in some cases on the opposite side, LLMs struggles with certain sorts of problems like Multistep reasoning(maths word problem during which multiple steps are required), symbolic manipulation or in commonsense reasoning. Recently Researchers at Google got here up with the concept Chain of thoughts to enhance the reasoning ability of the LLMs and enable the model to that are usually not solvable by standard prompting methods. The sort of prompting ends in higher accurate results. The chain of thought guides the model to resolve the issue because the human brain solves those problems. Consider one’s own thought process when solving a multi-step math word problem, where it’s typical to decompose the issue into intermediate steps and solve each before giving the ultimate answer. That is how the chain of prompting works.
5. (REASONING AND ACTING): React is a paradigm that integrates language models with reasoning and acting capabilities allowing for dynamic reasoning and interaction with the external environment to perform complex tasks. , It combines LLM to generate its own tasks, the reasoning to trace within the updated plan accordingly and likewise to execute those steps to get additional information from external sources. LLm with React prompt engineering is far more powerful than chatgpt because It allows gathering additional information from external sources by interacting with external APIs, knowledge bases or different environments.
6. : It’s one other prompting technique which is generalized over and encourages exploration of thoughts that function intermediate steps for general problem-solving with language models. A tree of thought maintains a tree of thoughts, where thoughts represent coherent language sequences that function intermediate steps toward solving an issue. This approach enables LLMs to self-evaluate the progress intermediate thoughts make towards solving an issue through a deliberate reasoning process. It enables systematic exploration of thoughts with lookahead and backtracking. It enables Large Language Models, like ChatGPT, to exhibit superior reasoning abilities. And rectify their errors autonomously while progressively accumulating knowledge.