Millions Invested in Prompt Engineering: AI and LLM Adoption Speeds Up

July 31, 2023

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Prompt engineering is a growing field that guides AI models to generate high-quality, relevant texts. Industries like healthcare, finance, marketing, and customer service are set to benefit from it.

Startups like are emerging in this space – it has recently raised healthcare artificial.

One of the most welcoming developments in this field is Microsoft's Semantic Kernel - it aids in prompt engineering, providing a common interface for experimenting with prompts.

MarketsandMarkets welcomes the encouraging development, and we look at the challenges here.


What is Prompt Engineering?

Prompt Engineering, also known as natural language programming, is a novel form of interaction between humans and computers. It involves using large language models as a new general-purpose computer where output is determined by prompt programming. This concept is similar to how Python is parsed on a regular computer.

Prompt Engineering is a new concept of a token-based computer overlaid on top of binary computing, which unlocks an entirely new level of capabilities. It is used to unlock these new capabilities. Practical ways to build this into production today for real-world commercial applications. Some of the applications of Prompt Engineering mentioned in include:

  1. Content Filter: It involves structuring a well-defined prompt to process user input, similar to how a form is structured to accept user input and then process it through a function.
  2. Recreating Grammarly: With prompt engineering, it's possible to recreate key parts of applications like Grammarly, which corrects grammar, spelling, etc., and explains to the user what was corrected.
  3. Formatting Data for Another Prompt: This involves handling the formatting of an email, with the full power of GPT, into a desired known format.
  4. Natural Language UI (Prompt Router): This involves creating a natural language UI router or a “prompt selector prompt”. This prompt will take the user’s natural language and choose the best well-defined prompt.
  5. Auto Upgrade Prompt: This involves self-supervising the prompt. It generates a set of questions about the prompt itself, generates a “Revised prompt” based on the questions it generated, and then compiles/executes the Revised Prompt.
  6. Overcoming Limitations: Prompt engineering can help overcome limits and make uses practical for specific styles. It can help overcome moments in time limitations.

Top industries that are set to benefit from prompt engineering

The top industries that are set to benefit significantly from prompt engineering are:

  1. Healthcare: Prompt engineering can be used to analyze medical records, generate reports, and assist with clinical decision-making. AI models can identify patterns in medical data that would be difficult for human doctors to spot, leading to earlier diagnosis and treatment of diseases, as well as improved patient outcomes.
  2. Finance: Prompt engineering can improve fraud detection, risk assessment, and investment analysis. AI models can identify fraudulent transactions based on patterns in financial data. For example, Goldman Sachs has developed a prompt engineering framework called FinBERT that can be used to identify fraudulent transactions.
  3. Marketing: Prompt engineering can create more personalized and engaging customer experiences. AI models can generate personalized product recommendations or create targeted marketing campaigns.  For example, IBM Watson has developed a prompt engineering framework called Watson Marketing that can be used to create personalized marketing campaigns.
  4. Customer Service: Prompt engineering can create chatbots that can answer customer questions and resolve issues more efficiently. For example, Amazon Lex has developed a prompt engineering framework called Lex V2 that can be used to create chatbots.

The startup, which focuses on helping companies improve their generative AI prompting, has raised a $5 million seed round. The startup has 40 paying customers today, with revenue increasing by around 25% to 30% per month.


What is Microsoft Semantic Kernel?

It is a tool developed by Microsoft that allows developers to interact with Large Language Models (LLMs) in a more controlled and efficient manner. It provides a common interface to experiment with different prompts and parameters across multiple models. Semantic Kernel is deeply integrated with Visual Studio Code, making it easy to integrate prompt engineering into existing development processes. You can create prompts directly in your preferred code editor, write tests for them using your existing testing frameworks, and deploy them to production using your existing CI/CD pipelines.

  1. Prompt Engineering: Prompts play a crucial role in communicating and directing the behavior of LLMs. They serve as inputs or queries that users can provide to elicit specific responses from a model. Effective prompt design is essential to achieving desired outcomes with LLM AI models. Prompt engineering, also known as prompt design, is an emerging field that requires creativity and attention to detail. It involves selecting the right words, phrases, symbols, and formats that guide the model in generating high-quality and relevant texts.
  2. Career in Prompt Engineering: Prompt engineering is a critical skill for anyone working with LLM AI models. It's also a skill that's in high demand as more organizations adopt LLM AI models to automate tasks and improve productivity. A good prompt engineer can help organizations get the most out of their LLM AI models by designing prompts that produce the desired outputs.
  3. Tips for Prompt Engineering: Becoming a skilled prompt engineer requires a combination of technical knowledge, creativity, and experimentation. Some tips to excel in prompt engineering include understanding LLM AI models, acquiring domain-specific knowledge, exploring different parameters and settings to fine-tune prompts, continuously analyzing the outputs generated by the model and iterating on prompts based on user feedback, and keeping up with the latest advancements in prompt engineering techniques, research, and best practices.
  4. Real-world Application: Once you've become familiar with prompt engineering, you can use Semantic Kernel to apply your skills to real-world scenarios. By combining your prompts with native functions and connectors, you can build powerful AI-powered applications.

The Semantic Kernel and prompt engineering are powerful tools in the field of AI and machine learning, enabling more efficient and effective interaction with LLMs.


Which LLM models can you train in GPT?

How to train your own Large Language Model (LLM) using a project called privateGPT. This allows you to create a language model trained on your own data, such as sales insights or customer feedback, without exposing this sensitive data to an AI provider like OpenAI. The process involves training your own LLM locally, eliminating the need to upload your data to the cloud.

Here are the key steps.

  1. Downloading privateGPT: You can download privateGPT from GitHub using the following link: You can either download the repository by clicking on the Code | Download ZIP button, or if you have git installed on your system, use the following command in Terminal to clone the repository: $ git clone
  2. Training the Model: However, typically, this would involve feeding your data into the model and running a training algorithm.

PrivateGPT is currently a proof-of-concept.


Best practices when training an LLM

These best practices represent a first step in collaboratively guiding safer large language model development and deployment. The organizations involved in developing LLMs continue to work with each other and with other parties to identify other opportunities to reduce unintentional harm from and prevent malicious use of language models.

  1. Prohibiting Misuse: The group recommends publishing usage guidelines and terms of use for LLMs that prohibit material harm to individuals, communities, and society. This includes building systems and infrastructure for enforcing these guidelines. Usage guidelines should specify domains where LLM use requires extra scrutiny and prohibit high-risk use cases.
  2. Mitigating Unintentional Harm: To mitigate unintentional harm, the recommended practices include proactive mitigation of harmful model behavior and documentation of known weaknesses and vulnerabilities. This includes comprehensive model evaluation to assess limitations, minimizing potential sources of bias in training corpora, and techniques to minimize unsafe behavior such as learning from human feedback.
  3. Thoughtfully Collaborating with Stakeholders: The recommendations encourage building teams with diverse backgrounds, publicly disclosing lessons learned regarding LLM safety and misuse, and treating all labor in the language model supply chain with respect.
  4. Best Practices vs. Real World: While these recommendations are well-meaning, they are abstract and there is no real way of enforcing them. However, LLM providers are likely aware of upcoming regulations, such as the EU AI Act expected around 2025, and this initiative could be seen as a way of aligning themselves for "soft compliance" ahead of time.
  5. Support from Other Organizations: The initiative has garnered support from other organizations such as Anthropic, the Center for Security and Emerging Technology, Google Cloud Platform, and the Stanford Center for Research on Foundation Models. Google, in its statement of support, affirmed the importance of comprehensive strategies for analyzing model and training data to mitigate the risks of harm, bias, and misrepresentation.

Does AI have memories?

While AI does not have "memories" in the human sense, it can use mechanisms like embeddings to provide a form of memory that helps provide context for processing queries.

  1. What are Memories?: In the context of AI and LLMs, memories provide broader context for your queries. They are a core component for how computers work, similar to the RAM in your laptop. Memories make computation relevant to the task at hand. Memories can be accessed in Semantic Kernel in three ways:
  2. Conventional key-value pairs: Similar to setting an environment variable in your shell, the same can be done when using Semantic Kernel. The lookup is a one-to-one match between a key and your query.
  3. Conventional local-storage: When you save information to a file, it can be retrieved with its filename. This is useful when you have a lot of information to store in a key-value pair.
  4. Semantic memory search: You can represent text information as a long vector of numbers, known as "embeddings." This lets you execute a "semantic" search that compares meaning-to-meaning with your query.
  5. How does semantic memory work?: Embeddings are a way of representing words or other data as vectors in a high-dimensional space. Similar words or data will have similar vectors, and different words or data will have different vectors. You take a sentence, paragraph, or entire page of text, and then generate the corresponding embedding vector. When a query is performed, the query is transformed to its embedding representation, and then a search is performed through all the existing embedding vectors to find the most similar ones.
  6. Why are embeddings important with LLM AI?: Embeddings are useful for breaking down large text into smaller pieces. You can do this by summarizing each page into a shorter paragraph and then generating an embedding vector for each summary. An embedding vector is like a compressed representation of the text that preserves its meaning and context. Then you can compare the embedding vectors of your summaries with the embedding vector of your prompt and select the most similar ones. You can then add those summaries to your input text as context for your prompt. This way, you can use embeddings to help you choose and fit large texts as context within the token limit of the model.

Top Large Language Models and investments

  1. LLM Developer Market: The LLM developer space has seen explosive growth in recent months, in part due to LLM-fueled applications like OpenAI‘s ChatGPT, which has reached nearly 1B monthly active users since its November 2022 launch. Other players in the space, such as Cohere and Anthropic, have also garnered heavy investor interest. For example, in May 2023, Cohere raised a $270M Series C round while Anthropic secured a $450M Series C round with backing from Google and Salesforce Ventures, among others. LLM developers have raised nearly $12B in equity funding so far in 2023, a 12-fold increase from the previous year. Microsoft’s $10B round to OpenAI in January has driven the surge, but 4 other LLM developers have also raised mega-rounds (worth $100M+): Cohere, Mistral AI, Adept, and Anthropic.
  2. Amazon's LLM for Alexa: Amazon is building a more “generalized and capable” large language model (LLM) to power Alexa, said Amazon CEO Andy Jassy during the company’s first-quarter earnings call. Although Amazon has had an LLM powering Alexa, the tech giant is working on one that is more capable than the current one. The Amazon executive believes that the addition of an improved LLM will help Amazon work toward its goal of building “the world’s best personal assistant,” but acknowledged that it will be difficult to do so across many domains.
  3. Investments in LLMs: Several tech giants are investing heavily in LLMs. For instance, Microsoft invested $10 billion in OpenAI, and Google has invested hundreds of millions in Anthropic. Other companies like AI21 Labs, Mistral AI, and Adept have also raised significant funding.
  4. LLM in Other Tech Companies: Major tech companies are looking to incorporate LLM-based improvements into their offerings to keep up with the fast-paced AI space. For instance, Apple is reportedly developing LLM-based improvements for Siri, and it's likely that Google is doing something similar for Assistant. Alphabet, Microsoft, and Meta have also.

The development and application of LLMs are experiencing significant growth and investment, with major tech companies and startups alike recognizing the potential of these models in various applications.

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