ChatGPT Basics

Within a single conversation, ChatGPT has access to the entire message history — every message you sent and every response it generated. This context is included in every new prompt, which is why the model can reference details from earlier in the conversation and maintain coherent multi-turn discussions. However, this access is bounded by the context window, meaning very long conversations eventually cause early messages to drop out of the model's active context.

When you start a new conversation, this context resets completely. The model has no access to previous conversations unless you use the Memory feature (which stores a limited set of facts across sessions) or a Custom GPT with uploaded documents. This means that detailed work developed over many messages in one conversation — decisions made, context established, drafts refined — is invisible in the next conversation.

This has practical implications for workflow design. Long, complex projects benefit from either staying within a single conversation thread or from explicit summarisation — copying key decisions and context from one conversation into the next. The Memory feature helps with persistent preferences and factual recall but is not designed to carry detailed project context.

Understanding this memory model prevents common frustrations. When ChatGPT seems to 'forget' a decision you made yesterday, it has not lost the information — it never had it in the new conversation. The solution is structural: use Claude Projects or ChatGPT's Custom GPTs for persistent context, or develop a habit of starting conversations with a brief context summary.

Custom GPTs allow you to create specialised versions of ChatGPT with persistent instructions, uploaded knowledge files, and custom actions. For teams that handle repetitive specialised tasks — brand-compliant content generation, code review against specific standards, domain-specific analysis — Custom GPTs encode expertise that would otherwise need to be re-explained in every conversation. They are the closest equivalent to having a trained team member available on demand.

The Code Interpreter (Advanced Data Analysis) feature gives ChatGPT the ability to write and execute Python code, process uploaded files, and generate data visualisations. This transforms it from a text-generation tool into a data analysis environment, useful for exploring datasets, creating charts, and running calculations with verified results rather than generated approximations.

Web browsing enables real-time information retrieval, partially addressing the training data cutoff limitation. However, web search results are integrated into the model's response without the citation transparency of dedicated AI-powered search tools like Perplexity, making verification of current-event claims more difficult. For research tasks requiring source accountability, dedicated search tools may be more appropriate.

Canvas mode provides a side-by-side editing environment for documents and code, allowing targeted edits to specific sections rather than regenerating entire responses. This feature is particularly valuable for iterative refinement workflows where you want to modify one paragraph without losing the rest of a carefully developed document.

The most common mistake is treating ChatGPT as an authority rather than a tool. The model generates confident, well-structured prose regardless of whether its content is accurate. For factual questions — especially about recent events, specific technical details, or niche domains — the confidence calibration skill of knowing when to verify externally is essential. Confidence in presentation is not evidence of accuracy.

Another frequent mistake is under-specifying prompts. ChatGPT will always generate a response, filling gaps in your instructions with reasonable defaults that may not match your intent. Spending thirty seconds adding constraints, examples, and format requirements to your prompt saves minutes of revision on the output. The output formatting principle of showing examples rather than describing formats applies strongly here.

Over-reliance on a single conversation thread leads to degraded performance in long sessions. As conversation history grows, the model's attention to early context diminishes. For complex projects, splitting work across focused conversations — each with a clear scope and a brief context summary — produces more consistent results than a single marathon thread.

Finally, professionals often underuse the system prompt and Custom Instructions features. These persistent settings apply to every conversation, making them ideal for specifying communication preferences, output formats, and domain context that would otherwise need repeating. Configuring these settings once eliminates repetitive context-setting across all future interactions.

ChatGPT's strengths lie in its broad feature set — Code Interpreter, web browsing, image generation, Custom GPTs, Canvas mode, and the GPT Store — packaged in a polished consumer interface. For users who need a single tool that handles a wide range of tasks with minimal setup, ChatGPT offers the most complete all-in-one experience. The Custom GPT ecosystem also provides access to community-built specialisations without requiring any technical configuration.

For tasks where persistent project context matters, Claude Projects provides a more structured workspace model where uploaded documents and instructions persist across all conversations within a project. For tasks requiring deep analysis of long documents, Claude's extended context window processes larger inputs in a single pass. For tasks demanding cited real-time research, Perplexity provides stronger source transparency than ChatGPT's web browsing.

The practical answer for most professionals is not choosing one tool exclusively but understanding which tool fits which task. ChatGPT for quick general queries and code prototyping, Claude for extended analysis and project-based workflows, Perplexity for research with source verification, and specialised tools like GitHub Copilot for code completion within the editor. The skill is matching the tool to the task rather than defaulting to whichever tool is already open.

Pricing and access tiers also influence tool selection. ChatGPT offers a free tier with usage limits and a paid tier with higher caps, model upgrades, and access to advanced features like Custom GPTs and the GPT Store. Understanding what capabilities are available at each tier prevents frustration from hitting limits on the free plan during a complex task. For teams, the Team and Enterprise tiers provide workspace management, longer context windows, and administrative controls that individual plans lack.