Have you ever opened a new chat on ChatGPT or Claude to do something you asked them to do yesterday, and they didn’t remember anything?
So you re explain:
- your tone of voice
- your audience
- your formatting rules
- your company context
- examples of what it should look like
You finally get a decent result. Then you close the tab, open a new chat next week, and start again from scratch.
Research from Microsoft and LinkedIn found that 75 per cent of knowledge workers now use AI regularly, and lost context is one of the top frustrations reported. (1)
Although recent versions of ChatGPT and Claude now include automatic memory, which helps them remember some things between chats, this is limited and chats are still mostly self-contained.
Here we will look at how LLM Projects can fix this by providing a shared memory across chats, and how each of the main platforms approaches it.
What is an LLM Project?
An LLM Project used on a basic level is a folder where you can group chats together. However the real value comes from the files you add to the project. These are referenced by any chat you start inside the project and can drastically improve the quality of the output.
As there is a built-in memory layer of saved files inside the project, you also don’t have to add this information every time you start a chat.
For example, you can add any relevant files for your chosen task to that memory, things like your tone of voice guide, templates, examples, or company guidelines.
Then every new chat you open inside that project automatically reads those files before it responds to you and gives you something that meets those guidelines.
Examples of project files
Some files you could upload to projects that can be useful are:
- reference documents
- examples and templates
- rules and formatting standards
- previous research
- completed work
A recruiter writing job descriptions might upload:
- brief for writing job descriptions
- their company tone of voice guide
- a preferred job description structure
- strong past examples of job descriptions
- hiring rules and compliance requirements
Now every new job description starts from a much stronger baseline without rewriting the brief every time. And you don’t have to brief it each time. It’s already briefed.
The same applies to:
- marketers creating campaigns or managing content calendars
- consultants writing reports or client proposals
- founders producing investor updates or internal SOPs
- sales teams creating outreach campaigns
- operators documenting workflows or onboarding materials
In practice, even a small amount of high-quality context dramatically improves the consistency and quality of the AI output in the chat.
You can think of it like having a new intern or a new joiner on the team. You need to give them all the information they need about the tasks they’re going to be performing for you so that they can do their best work.
The LLM needs the same instructions and the more information you can give it the better its work for you will be.
Project instructions
You can also write instructions directly into the project. These are not files, they are more like standing orders you give the AI about how to behave in every chat inside that project.
Instructions typically start with “You are a…” to set the role, then follow with specific rules about tone, format, and behaviour. For example:
“You are a recruitment consultant helping a specialist finance agency write job descriptions. Always write in a formal, direct tone. Always include a salary range. Never use the phrase ‘fast-paced environment.’ Keep descriptions under 400 words.”
Or for a marketing team:
“You are a content strategist helping a B2B consultancy produce articles for senior business leaders. Write in plain US English. No jargon. Keep paragraphs to three sentences or fewer. Always suggest a headline and a subheading with every draft.”
The AI reads these instructions before every single chat in the project.
So if your files give it the knowledge, your instructions give it the rules. Together, they help the AI to make the correct assumptions about what you want without your having to enter it from scratch.
Context in projects
This shared collection of files and instructions is called context. So instead of explaining what you want and who you are every time, the AI already has your context loaded. You just ask for what you need.
Not sure where to start? Ask the AI
Not sure what files or instructions to add to your project?
Ask your chosen LLM to create them for you. Open a chat and write:
“I want to build a project that does {insert your desired task}. Ask me any questions you need to perform this task well, then write me a full set of project instructions and tell me which files to upload.”
It will come back with a series of questions to gather all the information and assumptions it needs. Answer them and it will return a full set of instructions ready to paste in, plus a list of exactly what to add to the knowledge base.
When drafting the instructions for you the AI will cover a lot more scenarios that you hadn’t thought of which is good but make sure you check the instructions before uploading them.
You can then go, create a new project, paste the instructions and add the files to it. You can now go to that project every time you want to perform that task and start a new chat.
Projects in different AI platforms
Depending on the platform, projects go by different names:
- Projects (ChatGPT and Claude)
- Spaces (Perplexity)
- Gems and NotebookLM (Google)
Although they have different names they have the same core idea: persistent context (memory + instructions) across chats.
Get started with ChatGPT Projects
What are ChatGPT Projects
ChatGPT Projects are what OpenAI calls “smart workspaces” or “smart folders”. (1) You create a project, upload your files, add some instructions, and every chat inside that project uses the same shared context to create a result for you. (2)
How ChatGPT Projects work
Each project has three main parts:
Instructions
You write these once as throughly as possible. They define the role, tone, and rules.
For example:
“You are helping us write job descriptions for a specialist finance recruitment agency. Use a professional but direct tone. Never use the phrase ‘dynamic team’. Always include a salary range.”
Files
You upload reference documents, for example past job descriptions, templates, brand guidelines, research, or spreadsheets. ChatGPT reads these and can use them in every chat inside the project. (2)
Conversations
All related chats live in one place. You can drag old chats into a project or start new ones directly inside it. (3)
One thing worth knowing. ChatGPT also has a separate account‑wide Memory feature, which stores facts it learns about you and can apply them inside projects. That can help, but if old information leaks into a project it can also contribute to drift. You can control this when you set up the project by choosing Default or Project‑only memory. (4)(5)
How to get started
- Open ChatGPT and click the “New project” plus button in the left sidebar (2)
- Give your project a name and, if you want, choose a colour and icon
- Open Project settings and choose Default or Project‑only memory
- Add your project instructions: role, tone, rules
- Upload your files (PDFs, Word docs, spreadsheets, past examples)
- Drag in any existing chats that belong in this project
- Start a new chat inside the project and work from there
Example use cases
Job descriptions
Upload your brief, tone guide, Job Description template, and strong past examples. (6)
Content calendars
Upload brand guidelines, audience profiles, and any content strategy docs. Ask for a month of on‑brand posts.
Blog posts
Some small business owners use Projects to generate SEO blog posts in a consistent format and report that it has brought leads into their business. (7)
Market research reports
Combine your internal notes with ChatGPT’s live web search and ask it to fill a pre‑defined report outline.
Pros and cons ChatGPT Projects
| Pros | Cons |
|---|---|
| Strong at short, practical copy: emails, social posts, outlines (22) | Can sound more obviously AI on longer pieces (23) |
| Good at structuring tricky messages and turning rough notes into clear copy (23) | Struggles to hold a specific tone of voice over long documents (24) |
| More naturally punchy and accessible for broad audiences (25) | Long-form content often needs more editing for nuance and depth (26) |
| Versatile across many tasks with minimal setup (27) | In Default mode, account‑level memory can bleed into projects and contribute to drift. Use Project‑only memory setting for anything client or brand sensitive. |
Claude Projects
What are Claude Projects?
Claude Projects are self‑contained workspaces inside Claude.ai. Where ChatGPT Projects feel like “smart folders” for your chats, Claude Projects work more like a research system. You upload your documents once, and Claude searches across them in every conversation in that project. (6)
Claude is widely seen as the strongest of the four platforms for writing quality. In a blind test with 134 people, Claude won 4 out of 6 writing categories when readers did not know which AI wrote which answer. (7)
The free plan supports a small number of projects (currently five), and paid plans allow many more. (6)
How it works
When you upload documents to a Claude Project, one of two things happens automatically:
- Small number of files:
Claude loads everything directly into its working memory and refers back to it as you chat. - Larger document collection (RAG mode):
When your files are too big to hold in memory at once, Claude switches to Retrieval‑Augmented Generation. In simple terms, it works like a librarian. When you ask a question, it searches your documents, pulls out the most relevant chunks, and uses those to answer, instead of trying to read everything every time. You do not need to configure this, Claude detects when to switch. (8)
Inside a project, every chat can use:
- your project instructions
- your uploaded documents
- a small project memory that summarises what you have done in that project so far
Nothing from your general account memory is used inside projects.
How to get started
- Go to claude.ai and click Projects in the left sidebar.
- Click + New Project.
- Give it a name (this is just for you, Claude does not use it in answers).
- In the knowledge base panel, click + to upload documents (PDF, Word, CSV, text files, usually up to 30 MB each in the UI). (6)
- Click Set project instructions and write your guidelines.
- Start a chat. Your files and instructions are now active for every conversation in that project.
You can also paste text straight into the knowledge base, which is handy for guidelines that do not need a separate file.
Use cases
- Job descriptions
Upload your company values, tone guide, JD template, and strong past examples. One content marketer added a style sheet with rules such as “avoid AI buzzwords like ‘essential’ or ‘crucial’” and found Claude followed them reliably across drafts. (9) - Long‑form writing
Claude is particularly strong for nuanced writing. One writer summed it up: “Claude is honestly unmatched when it comes to storywriting. It understands your prompts in such an emotional manner that when you read it, you feel it.” (11) - Market analysis reports
Upload competitor research, market reports, and your brief. Ask Claude to identify key findings and write a report in your standard structure. - Brand content with rules
Add a style rules document to the knowledge base. Claude refers back to it in every chat, so you can enforce things like banned phrases, tone of voice, and formatting standards. (10) - Creative writing series
Upload your world bible, character notes, and past chapters. Claude helps you keep continuity across a whole series.
Pros and cons of Claude Projects
| Pros | Cons |
|---|---|
| Strong at editing and rewriting messy drafts (11) (12) | Default output is often too long and wordy (13) |
| Performs best on long, document-heavy work with lots of context and examples (14) (15) | Tone can feel generic and corporate without strong context and examples (16) |
| Often preferred for creative and narrative writing (17) | Can drift back into complex language even after being asked for simplicity (18) |
| Holds a consistent voice well across long pieces with a good style guide (19) (20) | Quality can vary after model updates (21) |
Perplexity Spaces
What is Perplexity Spaces
Perplexity was built as a research tool first. Its whole design focuses on searching the web and giving you clear answers with clickable sources so you can see where the information came from.
Perplexity Spaces are its version of projects. A Space combines:
- your own uploaded files
- your instructions
- Perplexity’s live web search
That means that every answer can draw from both what you know and the latest information on the internet. (12)
Spaces are available on Perplexity Pro and higher plans, which start at about 20 dollars per month. (19)
How Perplexity Spaces works
Each Space has three main parts:
- Sources
Your uploaded files, up to around 50 on the Pro plan. (12) - Instructions
How you want Perplexity to respond. For example: “explain in simple language”, “always cite at least three sources”, or “answer like a market analyst”. - Threads
Individual conversations inside the Space, which you can keep and search later.
When you ask a question, Perplexity:
- Looks at your files
- Searches the web
- Combines both into an answer with citations you can click
On Pro you can also choose which underlying AI model runs your Space, including GPT or Claude based options. (14)
How to get started
- In Perplexity, click Spaces in the left sidebar.
- Pick a template or create a blank Space.
- Click Add Sources to upload your files.
- Click Add Instructions to set tone and behaviour.
- Start a thread and begin asking questions.
Use cases
- Market research reports
Upload internal notes or spreadsheets, then ask Perplexity to combine them with recent market news and competitor updates, with all sources linked. - Recruitment market briefs
Upload a job brief and any sector notes, then ask for current salary ranges, competitor job ads, and summary talking points. - Competitor tracking
Ask for a weekly or monthly summary of news and public updates about a list of companies. - Newsletter research
Use a Space as the “research engine” behind a newsletter. Ask it to surface the most important stories in your niche each week, with links so you can check them. (13)
Pros and cons of Perplexity Spaces
| Pros | Cons |
|---|---|
| Combines live web research with your own files in one place | Spaces are only available on paid plans (19) |
| Every answer comes with visible, clickable citations | Less suited to long-form or creative writing than Claude |
| Built-in collaboration features for teams | Interface and ecosystem are smaller than ChatGPT’s |
| Lets you pick the underlying AI model on Pro | Some users report inconsistent experience as Perplexity rolls out new models and features (15) |
| Templates for common research and analysis tasks |
Google’s tools: NotebookLM and Gemini Gems
What they are
Google does not have one single “projects” feature. Instead it offers two main tools that you can combine:
- Gemini Gems
Reusable AI assistants that you configure with instructions and a persona, such as “job description writer” or “market research analyst”. They hold the rules, not your documents. (16) - NotebookLM
A notebook‑style research workspace. You upload your sources, and the AI works only from those sources. Every answer cites the exact passage it used. It does not add information from the general web. (17)
Google is also testing a Projects feature inside Gemini that is closer to ChatGPT Projects, but this is still in beta. (18)
How they work
Gemini Gems
You create a Gem, write its instructions, and optionally define a persona or style. Every time you open that Gem, it follows the same rules, regardless of the topic. It is essentially a saved “role” for the AI. (16)
NotebookLM
You create a notebook and add up to around 50 sources, each up to about 500,000 words, in formats such as PDFs, Google Docs, Slides, web pages and YouTube links. The model then answers questions using only those sources. Every answer includes citations pointing back to the original files. (17) (20)
One standout feature is Audio Overviews. NotebookLM can automatically produce a podcast‑style two‑person conversation that talks through the key ideas in your sources, which is ideal for processing long reports or research packs. (17)
A common workflow is:
- Use NotebookLM to upload and understand all your background material.
- Export or copy the key points.
- Use a Gem, or another model such as Claude or ChatGPT, to turn those findings into finished content in your preferred voice.
How to get started
NotebookLM
- Go to notebooklm.google.com and sign in with a Google account.
- Click New notebook.
- Click Add sources and upload documents or paste links.
- Start asking questions, or use the built‑in Notebook Guide to generate summaries, FAQs, or outlines.
Gemini Gems
- In the Gemini interface, open the Gems section.
- Click New Gem.
- Name the Gem and write your instructions (role, audience, tone, format).
- Use that Gem whenever you want help with that type of task.
Use cases
- Processing long reports
Upload multiple sector or research reports and ask NotebookLM to pull out common themes, contradictions, and key numbers, with citations. - Proposal writing
Put case studies and service descriptions into NotebookLM, then use a Gem or another model to turn the findings into a client‑ready proposal. - Interview and podcast prep
Upload a guest’s articles or transcript and have NotebookLM produce a briefing document and potential questions. - Competitor analysis
Add competitor websites and reports as sources, then ask NotebookLM to summarise positioning, messaging, and key moves.
Pros and cons of NotebookLM
| Pros | Cons |
|---|---|
| Excellent for research on your own sources. NotebookLM only answers from the documents you upload and cites the exact passages it used, which makes checking facts easy. (26) | Each notebook is a silo. NotebookLM cannot combine knowledge across notebooks, which can be awkward for very large projects. (28) |
| Audio Overviews for long documents. It can turn big packs of material into podcast-style summaries, which reviewers find genuinely useful for busy reading. (29) | No live web or automatic updates. It never looks beyond your uploads, and you have to refresh sources by hand. (30) |
| Good free tier and simple “saved roles”. The free NotebookLM limits are generous for solo work, and Gemini Gems let you save common roles and instructions for repeat tasks. (31) | Two tools to juggle. Research lives in NotebookLM, personas live in Gems, so the workflow feels less unified than a single “Projects” feature. (32) |
How does memory sharing work between chats in the same project?
As you start to use the project more and more to complete tasks (and hopefully save yourself hours every week in the process) you may start to wonder if the LLM is remembering any context from your previous chats inside the project.
The answer is yes but what it remembers and applies differs from model to model. And project files are not the only memory source being used to produce the output.
ChatGPT Project shared memory
In ChatGPT Projects, the model is not only using your project files. It is also:
- using some high level information it has learned about you from other chats outside the project (account level information)
- reusing certain patterns and decisions from earlier chats in the same project
This means hidden memories from outside the project can slip in and affect the results, even if they are not written in your project instructions. This can lead to drift and happens under the Default memory setting that is applied to the project.
If you want the project to focus as much as possible on the files and instructions for that task, set it to Project only memory when you first create the Project:
This stops account level memory from being used in that project. Unfortunately you cannot change this setting later or apply it to existing projects so make sure you apply it when you first create the project.
Claude Projects
Claude Projects also reuses some information between chats, but in a more contained way.
- Claude keeps a small project memory for each project
- New chats in that project can reuse things like the project goal or your preferred style
- General account memory from chats outside the project is never used inside projects
So Claude can remember some things across chats in a project, but nothing from your general account memory will bleed in.
Only what you put into the project, and what Claude has learned inside that project, can affect the results.
That said, if a single chat inside a project goes on for a long time, Claude can start to drift away from your original instructions. It is usually better to start a fresh chat for each new task, rather than running one very long conversation.
Perplexity Spaces
Perplexity Spaces do not have automatic shared memory between chats.
- Each new thread in a Space starts from the same files and instructions
- Perplexity does not learn from previous replies in that Space
If you want it to keep a new rule or structure, you have to add it to a file or to the instructions yourself.
Google NotebookLM
NotebookLM is the simplest of all.
- It does not keep any cross chat memory
- Every answer is based only on the sources in that notebook and the question you ask
If something is not in your sources, NotebookLM will not remember it later.
References
- Microsoft & LinkedIn – 2025 Work Trend Index
- OpenAI – 12 Days of OpenAI: Day 7 (Projects)
- OpenAI Help Center – Projects in ChatGPT
- The Verge – ChatGPT Projects are fancy folders for your AI chats
- OpenAI Help Center – Memory FAQ
- University 365 – Hidden context in ChatGPT Projects
- Claude Help Center – What are Projects?
- Prompt Revolution – Testing Claude Projects’ new RAG feature
- AI Blew My Mind – ChatGPT vs Claude vs Gemini compared
- Reddit r/content_marketing – Has anyone built a Claude project or AI agent?
- Reddit r/claudexplorers – How is Claude right now for creative writers?
- Perplexity Hub – A student’s guide to using Perplexity Spaces
- UF Business Library – What are Perplexity Spaces?
- Cogito Daily – Perplexity AI review 2026 (hands‑on)
- GamsGo – Perplexity AI Free vs Pro
- MindStudio – Gemini Notebooks vs Claude Projects vs ChatGPT Memory
- Reddit r/GeminiAI – New to Gemini, how to do ChatGPT‑like projects?
- DigitalOcean – What is NotebookLM?
- Atlas Workspace – NotebookLM Limitations (2026)
- G2 – Claude AI Review (2026)
- G2 – ChatGPT Review (2026)
- Zapier – Claude vs. ChatGPT: Which is best?
- Reddit r/ChatGPTPro – Best use case you had with ChatGPT and AI this year?
- Reddit r/ChatGPTPro – Training ChatGPT for social media manager and content