General use cases for LLMs
If LLMs are used ethically and responsibly, several general applications might be beneficial:
- Language processing – real time translation, summarisation, information extraction.
- Content creation – creative writing, text creation.
- Conversational AI – virtual assistants, customer service chatbots, learning assistants.
- Professional tools – code generation, legal document analysis, academic research.
- Creative applications – music lyrics, poetry, scriptwriting.
- Media application – video creation, audio creation.
- Code generation – writing code, error detecting, de-bugging code.
Prompt
To sum up the potential use cases for LLMs we asked the free version of Claude 3.7 Sonnet to:
‘Write a concise 100-word paragraph highlighting the key use cases for Large Language Models in modern business environments. Focus on practical examples that deliver measurable value. Maintain a professional tone for executives.’
This is what it said:
Response
‘Large Language Models are revolutionizing how organisations operate across domains. In customer service, AI chatbots handle inquiries 24/7, reducing wait times and increasing satisfaction. Content teams use LLMs to draft blogs, reports, and marketing copy in seconds rather than hours. HR departments streamline recruitment by automatically screening resumes and generating personalized communications. Software developers accelerate coding with AI-powered assistants that suggest solutions and explain complex algorithms. Legal teams extract key provisions from contracts and identify potential risks automatically. Research departments synthesize findings from thousands of documents instantly. When implemented thoughtfully, LLMs boost productivity, enhance decision-making, and create competitive advantage through automation of knowledge work.’
How would you rate the answer? Claude produced this answer within seconds, much quicker than most of us could write a similar answer. It is slightly over the word count at 104 words, but it does provide a summary of use cases for LLMs in a professional tone.
In the second course, Skills and strategies for using Generative AI, you learnt about prompting frameworks and prompt libraries, using that knowledge you could consider using an LLM for:
Content creation
- Document drafting, first drafts of reports, papers and case studies.
- Generating meeting agendas and detailed minutes and actions.
- Drafting internal communications, such as newsletters, marketing materials, website content, blogs and policy documents.
- Composing emails and summarising long email threads to extract key points and decisions.
- Simplifying content or generate content in a specific tone or style.
- Proofreading and suggesting improvements to the text.
- Generating response templates for common questions or enquiries.
- Creating presentations, slide decks, and content based on specific topics and audiences.
- Generating speaker notes with talking points and transitions.
- Suggesting data visualisation approaches for complex information.
Research assistance
- Summarising lengthy research papers and reports into actionable insights.
- Extracting relevant data points from multiple sources.
Examples of tools
- Perplexity is a search engine that uses LLMs to answer queries that include sources from the web and cite links with the text response.
- Elicit is an AI research assistant that researchers can use to support with literature reviews it searches academic papers from the Semantic Scholar corpus.
- Paid versions of LLMs like ChatGPT now offer a ‘deep research’ function where the LLM browses the web, reads multiple sources and extracts relevant information, it then analyses that information to provide a report with citations and explanations. For more information on deep research, you can read this blog The Differences between Deep Research, Deep Research, and Deep Research.
- NotebookLM is a personalised AI assistant, it is designed to help users better understand and interact with their own documents. NotebookLM allows you to upload sources (like Google Docs, PDFs or copied text), and then ask questions, generate summaries, or brainstorm ideas based on that content. Its key features include source-grounded responses – it only answers based on the documents you upload. It will produce auto-generated summaries, glossaries and FAQs, and it can help you compare and synthesise across different documents. It also allows you to generate an AI-powered audio conversation between two virtual hosts. They discuss the content you have uploaded, and the conversation is automatically generated based on your notes, documents or research materials.
Visual and audio support
- Image generation: AI systems such as DALL-E or Midjourney can create images based on textual descriptions.
- Audio content: podcast script creation, creating description narration for visual content to improve accessibility, interview question preparation and music composition.
- Video content: script creation, storyboarding assistance, YouTube titles and descriptions, content ideation and video editing support.
- Video post-production: automatic caption generation, content summarisation, thumbnail text generation, chapter marking and content tagging.
Creative assistance
- Idea generation: product ideas, new service offerings.
- Brainstorming: marketing strategies, event planning.
- Overcoming creative blocks: fresh perspectives or re-framing problems.
- Role-playing: using different personas to offer different viewpoints.
- Feedback and re-fining: reviewing your work, giving constructive feedback, edits and suggestions.
- Questioner: ask the LLM to suggest questions for you to consider on a topic.
Productivity
- Meeting summaries or transcripts.
- Training programmes, researching, and creating training.
- Creating internal FAQs and checklists.
- Translation of documents.
- Redaction of documents.
- Integrating into existing tools such as Outlook or add GenAI tools to browser extensions, for example, Chrome.
Different ways you can use GenAI tools
Listen to this audio clip in which Francine Ryan discusses the different ways you can use GenAI tools.
Transcript
Think about one way you might use a GenAI tool in your work or in your organisation and how it might help you.
Discussion
There may be lots of ways in which you can use an LLM in your work or in your organisation. However you use an LLM you need to understand the legal and privacy implications as well as how to prompt it, guide it and think critically about its outputs.
We asked ChatGPT 4 (April 2025) for some suggestions of which LLMs should be used for different tasks. You can see below what is suggested.
You can check for updates and improvements to LLMs through their release notes – read the latest updates to ChatGPT and Google Gemini. The technology is moving fast, and you should always do your research into the tools that you think are appropriate for you or your organisation. This is explored further in the eighth course, Preparing for tomorrow: horizon scanning and AI literacy.
Further reading
If you are interested, you can learn more about this topic from a table of different AI tools which gives a short description of how each of them works.
3 General use cases for GenAI
