What They Told You About Prompt Engineering is WRONG!

codebasics
10 Apr 202408:38

TLDRThis video challenges the notion that prompt engineering is simply about writing effective prompts for AI like ChatGPT. The speaker, from atck Technologies, argues that while effective prompts can boost productivity, the term 'prompt engineering' is misleading for everyday use. They explain that true prompt engineering involves guiding AI models to produce desired outputs, a skill required in specific roles within companies that utilize or develop AI models. The video delves into the actual job responsibilities, including customizing AI outputs and testing new models, and outlines the skills needed, such as domain knowledge and programming. It concludes with a cautionary note on the limited job market for prompt engineers and advises viewers to be wary of courses promising high earnings in this field.

Takeaways

  • 😀 Prompt engineering is often misrepresented as simply writing effective prompts for AI like ChatGPT.
  • 🤔 The video debunks the idea that prompt engineering is a lucrative career where one can earn hundreds of dollars for writing prompts.
  • 🏢 The speaker, from atck Technologies, asserts they have no need for a dedicated prompt engineer, suggesting it's not a common role.
  • 🔍 The formal definition of prompt engineering is to write prompts that guide an LLM to produce a desired output.
  • 💼 There are two paths in prompt engineering: using ChatGPT effectively in daily tasks, and as a specialized role in companies working with LLMs.
  • 📈 Effective prompts can enhance productivity, as illustrated by the example of planning a trip to Goa with personalized preferences.
  • 💼 Companies like Walmart and Reliance, which use LLMs, or those building LLMs like OpenAI and Google, may hire prompt engineers.
  • 🛠️ Prompt engineers may need to write prompts for automated systems, requiring knowledge of few-shot learning and domain expertise.
  • 🧑‍💻 The role may also involve testing LLMs, writing test cases, and evaluating outputs using metrics like cosine similarity.
  • 📊 Skills for a prompt engineer include linguistic understanding, communication, problem-solving, domain knowledge, programming, and statistical analysis.
  • 🔍 As of April 2024, the demand for prompt engineers is low, with only 20 jobs found in India on LinkedIn, suggesting caution for those considering it as a career path.

Q & A

  • What is the formal definition of prompt engineering?

    -The formal definition of prompt engineering is writing prompts in such a way that you can guide a large language model (LLM) to produce a desired output.

  • Why does the speaker claim that many videos on YouTube about prompt engineering are misleading?

    -The speaker claims that many YouTube videos are misleading because they suggest that prompt engineering is simply about writing effective prompts for AI like ChatGPT, and that one can get paid hundreds of dollars for this, which is not the case in real-world AI companies.

  • What are the two ways in which prompt engineering can be done according to the video?

    -Prompt engineering can be done as a ChatGPT user to write effective prompts for which you typically get paid nothing, and as a proper prompt engineer career role for which you can get paid.

  • How does the speaker describe the role of a prompt engineer in a company?

    -The speaker describes the role of a prompt engineer as working in companies that use LLMs like Walmart or companies that build or customize LLMs like OpenAI and Google. Their job involves writing prompts that guide the LLM to produce desired outputs in a programmatic way.

  • What is the difference between using ChatGPT for personal use and using it in a professional setting according to the video?

    -Using ChatGPT for personal use involves writing effective prompts to boost productivity in day-to-day tasks, whereas using it in a professional setting involves writing prompts that guide the LLM in an automated and programmatic way for specific tasks like data extraction.

  • What is 'few-shot learning' as mentioned in the video?

    -'Few-shot learning' refers to providing detailed examples that can guide the large language model to understand and perform tasks effectively.

  • Why does the speaker believe that the term 'prompt engineering' is an overkill for using effective prompts with ChatGPT in daily life?

    -The speaker believes that the term 'prompt engineering' is an overkill for daily use with ChatGPT because it implies a level of engineering work that is not present when simply using the AI for personal tasks.

  • What skills are required for a prompt engineer according to the video?

    -The skills required for a prompt engineer include understanding of linguistics, psychology, strong communication and problem-solving skills, domain understanding, programming skills, knowledge of LLM evaluation metrics, and some statistical skills.

  • What is the current demand for prompt engineers as per the video?

    -As of April 2024, the demand for prompt engineers is not high, with only 20 jobs found on LinkedIn in India.

  • What advice does the speaker give to those considering enrolling in prompt engineering courses?

    -The speaker advises caution and thorough research before enrolling in any prompt engineering courses that promise high earnings, as the demand for such roles is currently low.

Outlines

00:00

🤖 The Myth of Prompt Engineering as a Lucrative Career

The speaker begins by challenging the notion that prompt engineering, or writing effective prompts for AI like ChatGPT, is a lucrative career path. They argue that the idea of being paid hundreds of dollars for such tasks is misleading. The speaker shares their own experience from their company, ATCK Technologies, where they have not found the need to hire a dedicated prompt engineer. They then delve into the formal definition of prompt engineering as guiding an LLM to produce a desired output and discuss the two ways of doing prompt engineering: as a user to enhance productivity or as a professional role within companies that utilize or develop LLMs. The speaker emphasizes that while using effective prompts can boost productivity in daily tasks, labeling it as 'engineering' might be an overstatement.

05:01

🔍 The Role and Skills of a Prompt Engineer

This paragraph delves deeper into the professional role of a prompt engineer, distinguishing between companies that use LLMs like Walmart and those that build or customize them, such as OpenAI and Google. The speaker explains that prompt engineers in user companies might need to write prompts that guide the AI to produce outputs in specific formats, like JSON, for downstream processing. They also discuss the concept of 'few-shot learning,' where detailed examples are provided to guide the LLM. The speaker then moves on to describe the skills required for a prompt engineer, including linguistic understanding, communication, problem-solving, domain expertise, programming skills, and knowledge of LLM evaluation metrics and statistics. The paragraph concludes with a discussion on the current job market for prompt engineers, noting that as of April 2024, the demand is low, and advising caution for those considering prompt engineering courses promising high earnings.

Mindmap

Keywords

💡Prompt Engineering

Prompt engineering refers to the practice of crafting prompts in a way that guides a large language model (LLM) to produce a desired output. In the context of the video, it is debunked as a career that pays hundreds of dollars for merely writing effective prompts for AI like ChatGPT. The video suggests that while effective prompts can enhance productivity, the term 'engineering' might be an overstatement for everyday use.

💡ChatGPT

ChatGPT is mentioned as an AI platform where users can write prompts to receive responses. The video discusses how writing effective prompts for ChatGPT can be beneficial for personal use, but it does not warrant a specialized engineering role in most cases. It is used as an example to illustrate the difference between casual prompt writing and professional prompt engineering.

💡Large Language Model (LLM)

An LLM is a type of artificial intelligence model that is trained on a large amount of data to understand and generate human-like text. The video explains that prompt engineering involves guiding an LLM to produce specific outputs, which is more complex than simply writing queries for a search engine or a chatbot.

💡Persona

In the video, 'Persona' is used to describe a specific statement within a prompt that provides personal preferences or characteristics to tailor the AI's response. For instance, stating 'I am a vegetarian' as part of a prompt to customize a travel plan according to dietary preferences.

💡Task

The 'Task' in prompt engineering is the explicit instruction given to the AI to perform a certain action or generate a specific type of output. The video uses the example of planning a trip to Goa, where the task would be to create a travel itinerary.

💡Context

Context in prompt engineering is the additional information provided to the AI to help it understand the nuances of the task and persona. It aids in producing a more accurate and customized response. The video gives an example where context about being a vegetarian and disliking adventurous activities is provided to refine travel suggestions.

💡Few-shot Learning

Few-shot learning is a technique where an AI model is provided with a few examples to learn from, rather than requiring extensive training data. In the video, it is mentioned as a skill needed for prompt engineers to effectively guide LLMs by providing detailed examples within prompts.

💡Domain Understanding

Domain understanding is the knowledge and expertise in a specific area or industry, which is crucial for prompt engineers to write effective prompts. The video explains that prompt engineers need to have a deep understanding of the domain they are working in to provide relevant and accurate prompts.

💡Cosine Similarity

Cosine similarity is a metric used to measure the similarity between two text paragraphs based on their semantic meaning. In the video, it is discussed as a method for evaluating the output of an LLM against expected answers during testing and quality assurance processes.

💡Prompt Evaluation

Prompt evaluation involves assessing the effectiveness of prompts in eliciting the desired responses from an LLM. The video discusses how prompt engineers may need to write test cases and use metrics like cosine similarity to ensure the AI's responses are accurate and relevant.

💡Career Prospects

The video explores the career prospects of prompt engineering, suggesting that as of April 2024, the demand for such roles is relatively low. It advises viewers to be cautious about enrolling in courses that promise high earnings from prompt engineering, as the job market may not currently support such claims.

Highlights

Prompt engineering is often misunderstood as simply writing effective prompts for AI like ChatGPT.

The notion that prompt engineering can command high salaries is largely a myth.

At 'atck Technologies', there's no need for a dedicated prompt engineer; AI engineers handle prompt creation.

Prompt engineering involves guiding an LLM to produce a desired output through effective prompts.

Prompts can be customized for personal preferences to enhance productivity in daily tasks.

The term 'prompt engineering' might be an overstatement for everyday use of ChatGPT.

Prompt engineers work in companies that use LLMs like Walmart or build/custom LLMs like OpenAI and Google.

In healthcare data extraction, prompt engineers help convert images to text and extract meaningful information.

Customizing LLM outputs often requires explicit instructions and context to streamline downstream processing.

Few-shot learning is crucial for prompt engineers to guide LLMs with detailed examples.

Domain understanding is key for prompt engineers to communicate effectively with LLMs.

Prompt engineers may need to write test cases for new LLMs, similar to QA engineers in software.

Adversarial testing is part of evaluating LLMs to ensure they respond appropriately to tricky questions.

Cosine similarity is used to measure the semantic similarity between expected and actual LLM outputs.

Skills required for a prompt engineer include linguistic understanding, communication, problem-solving, domain expertise, programming, and statistical skills.

As of April 2024, the demand for prompt engineers is low, with only 20 jobs found on LinkedIn in India.

AI is evolving rapidly, and the role of prompt engineer may transform, affecting future demand.

The video advises caution against enrolling in courses promising high earnings from prompt engineering.