How is the use of chatgpt in the workplace? Best practices and examples

Over the past two years ChatGPT has stopped being an experimental toy and become a visible — often indispensable — part of many enterprise workflows. Employees across functions use it for drafting emails, writing and reviewing code, summarizing meetings, generating marketing ideas, and automating repetitive tasks. Large vendors have baked similar generative-AI assistants into core productivity suites (most notably Microsoft’s Copilot offerings), and platform-level improvements (model upgrades, enterprise features, data residency controls) have made it easier for organizations to adopt ChatGPT-like systems in ways that meet compliance and security needs. These product and policy moves have accelerated workplace integration and made ChatGPT-style assistants obvious to anyone who spends time in knowledge work.

By the way, you can try CometAPI which offers access to GPT-5.1, GPT-5, and over 100 AI models for chat, image, music, and video generation. Its API price costs 80% of the ChatGPT API.

Why is ChatGPT becoming so obvious in the workplace?

ChatGPT (and sibling LLM-based assistants) have reached commodity-level usefulness for common knowledge tasks — writing, summarization, search, triage, first-draft coding, meeting note generation, and conversational help inside collaboration tools. This is reasons made the transition from experimental to obvious:

1. Productivity gains: Automating repetitive text work, drafting and iteration, and accelerating developer workflows.
2. Scaling knowledge work: Turning tribal knowledge and documentation into searchable, generative assistants that help new hires and reduce context-switching.
3. Competitive advantage: Faster content production, quicker data synthesis for decisions, and novel automation of routine processes (e.g., contract review, code scaffolding).

What are the main editing workflows?

There are three practical editing flows you’ll use frequently:

1. Text-driven edits and re-generations — change a shot by rewriting the prompt or applying new instructions to the same scene.
2. Reference-image guided editing (“Ingredients to video”) — you supply up to 3 images to preserve a character or object across generated frames.
3. Frame interpolation (First & Last frame) — give a start and end image and Veo generates the transition sequence between them (with audio if requested).
4. Scene extension — extend an existing Veo-generated (or other) clip by generating a connecting clip that continues from the last second of the previous clip.
5. Object insertion/removal and other Flow editing tools — some Flow UI features (object insertion/removal, doodle prompting, camera angle reshoots) are being added on top of Veo capabilities and can help with frame-level retouching in a GUI.

Below I walk through the most common programmatic and UI workflows: editing in Flow (creator UI), using the Gemini app (quick generation), and using the Gemini API / CometAPI API programmatically (for production and automation).

How does ChatGPT actually show up in day-to-day workflows?

In which everyday tasks is it already obvious?

• Emails and communication: Drafting, rewriting for tone, condensing long threads into action items.
• Meeting summaries: Live transcription + summary tools reduce the need for manual note-taking.
• Code assistance: Autocompletion, bug-finding, unit-test generation, pull-request drafts.
• Documentation and knowledge search: Converting internal docs into conversational Q&A and structured knowledge.
• Content and marketing: Drafting blog posts, ad copy, A/B test ideas and social media calendars.
• Operational automation: Generating scripts, SQL queries, or small automation routines from natural language instructions.

Each of these shows up not only as a “person using ChatGPT in a browser” but also as built-in features in enterprise software (e.g., Copilot in Office apps) and as integrated API calls into custom internal tools. Microsoft’s trend toward embedding Copilot into Word, Excel, and Teams is a clear signal that vendors consider generative assistants core functionality, not an optional plugin. Teams are using ChatGPT as an amplifier across a predictable set of tasks. Below are high-impact examples and brief implementation patterns you can adopt immediately.

Note: the code below uses the modern OpenAI client patterns (client-based Python). We recommend using the CometAPI API, as the discount offers excellent value. Simply replace your OpenAI key with the CometAPI key, and then switch between the CometAPI chat and response endpoints.

Editing, drafting, and creative-adjacent tasks

• Emails, job descriptions, proposals: turn bullet points into polished drafts.
• Marketing copy and A/B variants: rapid ideation and localized variants.
• Policy & documentation drafting: generate first drafts and alternative phrasings.

Python: Draft and personalize an internal email (Responses API)

# save as ai_email_draft.py
# Requires: pip install openai (or the latest `openai` package)
import os
from openai import OpenAI

API_KEY = os.getenv("OPENAI_API_KEY")
if not API_KEY:
    raise RuntimeError("Set OPENAI_API_KEY in environment")

client = OpenAI(api_key=API_KEY)

def draft_email(name: str, role: str, topic: str, tone: str = "professional", bullets=None):
    """
    Produce a first-draft internal email.
    """
    bullets = bullets or []
    instruction = (
        f"You are a helpful assistant that writes clear internal emails. "
        f"Write an email to {name} ({role}) about: {topic}. "
        f"Tone: {tone}. Include an executive summary (1 sentence), "
        "2-3 action items, and a short closing line."
    )

    # Responses API: instructions + input
    response = client.responses.create(
        model="gpt-4o-mini",  # pick a model your org has access to
        instructions=instruction,
        input=[
            {
                "role": "user",
                "content": [
                    {"type": "input_text", "text": "Draft for internal use."},
                    {"type": "input_text", "text": "\n".join(bullets)}
                ],
            }
        ],
        max_output_tokens=700,
    )

    # The API returns structured outputs — use output_text for quick extraction
    draft = response.output_text
    return draft

if __name__ == "__main__":
    print(draft_email("Aiko Tanaka", "Product Manager", "Q1 roadmap alignment", bullets=[
        "- Provide status on feature X",
        "- Confirm owners for initiative Y",
    ]))

Integration notes: run this server-side; never embed the API key in a client app. Save drafts to your document store with metadata for audit.

Meeting summarization and action-item extraction

A common pattern: a meeting transcription (from Zoom, Teams) is fed into the assistant which returns a concise summary and assigned action items.

Python example — meeting summarizer (simple, production would add auth/audit and rate limiting):

# meeting_summarizer.py — simple example
import os
import openai   # pip install openai
from typing import List

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
openai.api_key = OPENAI_API_KEY

def summarize_meeting(transcript: str) -> str:
    prompt = f"""
    You are an expert meeting summarizer.
    Produce:
    1) A 3-sentence summary of the meeting.
    2) A bulleted list of action items in the form: [Owner] - [Action] - [Due date or timeframe]
    3) 3 suggested next steps for leadership.
    Transcript:
    {transcript}
    """
    resp = openai.ChatCompletion.create(
        model="gpt-4o",            # replace with your organization's model
        messages=[{"role": "user", "content": prompt}],
        max_tokens=400
    )
    return resp["choices"][0]["message"]["content"]

# usage:
# transcript = load_transcript("meeting_123.txt")
# print(summarize_meeting(transcript))

(In enterprise settings: run this inside a function that logs requests, stores outputs in the user’s record, and enforces data residency & retention rules.)

Customer support triage

Automatic classification of tickets, suggested answer drafts, knowledge base search. These reduce first-response time and let agents focus on complex issues.

Code assistance and developer productivity

• Generate unit tests, refactor suggestions, inline code explanations.
• Many engineering teams are already using assistants during code review and PR generation.

Code example — simple prompt to generate unit tests:

prompt = """
You are a python unit test generator.
Given the function below, create pytest unit tests that cover normal, edge, and error cases.
Function:

def add(a: int, b: int) -> int:
return a + b

"""
# send prompt using the same ChatCompletion pattern as above

How does ChatGPT change workflows and worker roles?

AI shifts the unit-of-work: tasks that were previously atomic (drafting, summarizing, triage) become augmented: the human provides intent, the assistant drafts, and the human edits and approves. Research indicates companies are investing heavily in AI, but only a small share say they have reached maturity — the big opportunity is orchestration: how managers redesign workflows so human+AI teams optimally collaborate.

Interactions vary by role:

• Developers: Ask for code snippets, refactors, explanations of library behaviors, or automated tests.
• Marketers & communicators: Request tone variants, campaign outlines, or keyword-rich copy.
• Analysts & ops: Generate SQL or data-transformation scripts, ask for data-extraction templates.
• Managers & PMs: Use it for one-pagers, stakeholder communications, and to convert meeting outputs into action lists.

This diversity of use cases makes ChatGPT visually present: you’ll find ChatGPT conversation windows, Copilot panes in Office apps, automated Slack bots backed by LLMs, or internal dashboards with “Ask our docs” chatboxes — all of which are unmistakable to employees and IT alike.

Job redesign patterns (practical examples)

• Legal teams: assistants draft initial briefs, but lawyers do legal reasoning and finalization.
• Customer success: assistants propose replies and identify churn risk, while human agents manage emotional and strategic conversations.
• Product & engineering: engineers use assistants for scaffolding (tests, docs) while focusing on architecture and systems thinking.

Measuring role impact (sample metrics):

• Mean time to first response (support).
• Draft-to-final edit ratio (content teams).
• PR cycle time for engineering.
• Number of tickets escalated (triage accuracy).

Advanced practices and optimizations

Prompt patterns that reduce hallucination

• Explicit grounding: “Use only the documents listed in sources below. If you cannot answer, say ‘I don’t know’.”
• Structured output requests: require JSON or numbered sections so you can parse and automate.
• Few-shot examples with correct and incorrect examples to set expectations.

Example: a structured prompt for product requirements:

You are a product analyst. Using only the following three requirement documents (DOC1, DOC2, DOC3), produce:
1) 1-paragraph summary of the product goal.
2) JSON array of feature names with priority (high|med|low).
If information is missing, return an empty array.

Validation & automated checks

• Use unit tests for prompts (golden prompts).
• Compare assistant outputs against a curated knowledge base with semantic similarity checks (RAG + confidence scores).
• Automate a human review step for outputs below a quality threshold.

Conclusion — Is ChatGPT now obvious at work, and what next?

Yes — ChatGPT is obvious at work because it is embedded, instrumented, and now governed in enterprise contexts. Vendors have moved from experimental feature flags to hardened integrations (Copilot, company knowledge, regional hosting), and the research and industry reports show rapid adoption and serious interest in scaling responsibly.

Bottom line for leaders: treat assistants like a new platform: define clear use cases, lock down data & governance first, pilot to measure impact, and then scale with guardrails. The gains (time saved, faster drafts, better triage) are real — but so are the legal and safety obligations. Do both well, and the assistant becomes not just obvious, but indispensable.

To begin, explore the model capabilities of CometAPI in the Playground and consult the Continue API guide for detailed instructions. Before accessing, please make sure you have logged in to CometAPI and obtained the API key. CometAPI offer a price far lower than the official price to help you integrate.

Ready to Go?→ Sign up for CometAPI today !

If you want to know more tips, guides and news on AI follow us on VK, X and Discord!