Prompt engineering is one of the most important skills for anyone who wants to use artificial intelligence effectively. It consists of creating clear instructions to guide AI models toward more useful, accurate, and goal-aligned responses.

Before understanding prompts, it is important to understand foundation models, also known as FMs.

What are foundation models?

Foundation models are artificial intelligence models trained on large volumes of data and capable of performing many different tasks. They use deep neural networks to handle complex tasks and can be adapted for different use cases.

Some of their main applications include:

• Text generation
• Text summarization
• Information extraction
• Image generation
• Chatbots
• Question answering
• Creation of more specialized models

Examples of foundation models include Amazon Titan, Amazon Nova, Meta Llama 2, Anthropic Claude, AI21 Labs Jurassic-2 Ultra, and others.

The main difference between FMs and traditional machine learning models is their general capability. Instead of being created for only one specific task, they can serve as a starting point for many different applications.

Training, fine-tuning, and prompts

Foundation models go through different stages to become useful.

During training, they analyze large datasets to learn patterns, relationships between words, context, and meaning. This process can involve unsupervised learning and also reinforcement learning from human feedback, known as RLHF.

RLHF helps align the model’s behavior with human preferences. In this process, people evaluate the model’s responses, and this feedback is used to improve the AI’s behavior.

After training, fine-tuning may happen. Fine-tuning adapts a model to more specific tasks or domains. For example, a general model can be fine-tuned to answer legal, financial, or medical questions more effectively.

Prompt engineering, on the other hand, does not change the model. It works by improving the way you write instructions to get better responses.

The role of NLP

Natural language processing, or NLP, is the area of machine learning that allows machines to interpret and manipulate human language.

NLP works with text and speech, analyzing intent, sentiment, and meaning. Traditional techniques include:

• Tokenization
• Stemming
• Lemmatization
• Removal of irrelevant words
• Part-of-speech tagging
• Named entity recognition
• Speech recognition
• Sentiment analysis

However, modern LLMs can handle many of these steps more directly, without relying so heavily on manual intermediate processes.

RNNs and their limitations

Before transformers, recurrent neural networks, known as RNNs, were widely used in sequential tasks.

They have a memory mechanism, which allows them to store information from previous inputs. This helps in tasks such as language processing, speech recognition, and machine translation.

But RNNs also have limitations. They are slower to train, more complex, and do not take full advantage of parallelization during training.

The importance of transformers

The transformer architecture was one of the most important advances in the development of modern LLMs.

A transformer has an encoder, which turns input text into internal representations, and a decoder, which uses those representations to generate text output.

The major advantage is that transformers can process the entire input at once, instead of analyzing word by word sequentially. This allows faster and more efficient training, although it requires significant computing power.

Today, most large language models use transformer-based components.

What is prompt engineering?

Prompt engineering is the practice of creating instructions so that AI models deliver better responses.

A good prompt can include some main elements:

• Instruction: explains the task the model should perform
• Context: provides information to guide the response
• Input data: presents the content that will be analyzed
• Output indicator: defines the expected response format

For example, instead of simply asking “summarize this text,” you can ask: “Summarize this text for a blog, using simple language and up to three paragraphs.”

The second version is clearer, more contextualized, and gives the model better direction.

How to create more effective prompts

An effective prompt needs to be clear, direct, and well structured.

Be clear and concise

Avoid ambiguous commands, isolated words, or confusing sentences. Use natural language and explain exactly what you expect.

Less effective prompt:

“Calculate the total sum of the subsequent sequence of numerals: 4, 8, 12, 16.”

More effective prompt:

“What is the sum of these numbers: 4, 8, 12, 16?”

Include context when necessary

Context helps the model better understand the scenario.

Less effective prompt:

“Summarize this article.”

More effective prompt:

“Provide a summary of this article to be used in a blog.”

When asking for a business analysis, include the type of business, the market, and the goal of the analysis. The more relevant the context, the better the response tends to be.

Define the response format

If you need the answer as a list, summary, table, poem, question, or short sentence, say that in the prompt.

Less effective prompt:

“What is the capital?”

More effective prompt:

“What is the capital of New York? Provide the answer in one complete sentence.”

Conclusion

Prompt engineering starts with a simple idea: the quality of the response depends a lot on the quality of the instruction.

Foundation models are powerful, but they need direction. When you understand how they work, how they were trained, and how they interpret language, it becomes easier to create clear, useful, and safe prompts.

In the end, good prompts connect human intention with AI capability.


References: https://skillbuilder.aws/learn/VF6H4SZ1BU/foundations-of-prompt-engineering/