The most-used LLM in Indian CA firms today. What it’s genuinely useful for in audit work, where it falls short, and the features (GPTs, Code Interpreter, Custom Instructions, Memory) that matter specifically for CA practice.
ChatGPT is OpenAI’s consumer-facing AI assistant. It’s the most widely adopted LLM in Indian CA firms — partly because of brand familiarity, partly because of the GPTs ecosystem, and partly because the free tier is genuinely useful for methodology and drafting work.
The single most relevant fact for Indian CAs: ICAI’s CA-GPT is built on the GPTs framework (i.e. you can access it from inside ChatGPT). It comes with 15+ specialised CA-domain GPTs covering auditing standards, internal audit, GST, direct tax, ethics, and sustainability reporting. Members get 20 free prompts per day; INR 499 / month gets unlimited. If you have an ICAI membership, you should be using CA-GPT before any other paid tier.
Engagement letters, MRLs, queries to management, audit observations, CARO clauses — ChatGPT produces a workable first draft in seconds. The voice is competent without being formal-stiff. Edit time is usually shorter than start-from-blank-page time.
Asking ChatGPT to brainstorm risks under SA 315, generate fraud schemes under SA 240, or walk through the considerations for SA 320 materiality benchmarks — it does this well. Treat it as a sparring partner for the methodology you already know.
GPTs are pre-configured assistants for specific tasks. ICAI’s CA-GPT is the canonical example for Indian audit work — separate sub-GPTs for Auditing and Assurance Standards, Internal Audit, GST and Indirect Taxes, Direct Taxes, Ethical Standards, Financial Reporting Review, Peer Review, Sustainability Reporting. Each one is grounded in ICAI’s curated knowledge base for that domain. Use it for standards-grounded questions where you’d otherwise have to read the bare standard.
On Plus and above, the Code Interpreter writes and runs Python on data you upload. For an auditor: upload an anonymised CSV of journal entries, ask for entries that meet a specific pattern (weekend postings, round-number entries, debits to revenue, manual JVs above a threshold), get the filtered list back. Sample-size calculations, ratio analysis, ageing-bucket categorisations — all work cleanly.
A persistent set of instructions that every conversation inherits — “I am a Chartered Accountant in India working under the Companies Act 2013 and SAs issued by ICAI; respond with that context, cite standards where applicable, use Indian English and ₹ for currency.” Set once, applied everywhere. Saves repetitive framing.
ChatGPT remembers facts you tell it to remember across conversations — “I work in a 12-partner firm in Bengaluru, our biggest engagement is in the manufacturing sector, we use Tally Prime.” These persistent facts make every subsequent prompt more relevant. Memory can be turned off (and we recommend it stays off for any prompt that approaches client-specific data).
Fit map. Strong fits in bold.
See the Audit Prompt Library for the broader set. A few that work particularly well in ChatGPT:
About me: I am a practising Chartered Accountant in India. My firm is mid-sized (10-30 partners) and our engagements are predominantly statutory audit under the Companies Act 2013, tax audit under Section 44AB, and internal audit under Section 138. We use Tally Prime for client data and Microsoft 365 for documentation. How I want responses: - Respond in Indian English. Use ₹ for currency, lakh/crore for figures. - Cite the relevant SA / Ind AS / Companies Act section where applicable. - Default to concise output — bullets where useful, no preamble. - Where my question is ambiguous, ask one clarifying question rather than guess. - Never use my client's name, PAN, GSTIN or any other identifying detail in your responses, even if I include it in the prompt — anonymise. - If you don't know something, say so. Don't make up paragraph numbers or section references.
For an Indian [SECTOR — e.g. wholesale auto-parts distribution] company with annual turnover in the ₹100-300 crore range, brainstorm the risks of material misstatement at the assertion level. Cover: 1. Financial statement level risks (fraud risk, complex transactions, judgement-heavy estimates) 2. Account-level risks for each major balance sheet line item 3. Specific Indian-regulatory risks (CARO 2020 reportable items, Section 13(3) related-party concerns, IFC under 143(3)(i)) For each, name the relevant assertion (existence / completeness / accuracy / valuation / cut-off / classification) and the SA that governs the response. No entity-specific data — methodology only.
I'm uploading an anonymised CSV of journal entries (columns: entry_no, date, account_dr, account_cr, amount, user_id, narration). For an Indian statutory audit under SA 240, flag entries that meet any of the following patterns: 1. Posted on Saturday, Sunday or a public holiday (assume 2025-26 Indian holidays) 2. Manual JVs by user IDs other than the regular accounting staff (I'll list them in chat) 3. Round-number entries above ₹1 lakh 4. Debit entries to revenue accounts 5. Entries reversed within 7 days Return a Python notebook output with a table per pattern, plus a summary count. Don't compute on actual amounts — just flag and present.
Five tiers Indian auditors realistically encounter:
CORAA does not endorse any specific AI tool. This guide describes how Indian CAs use the named product in audit work — what tends to work, what tends not to, and the practical considerations around client data. It is not an integration guide, an affiliate page, or a recommendation. You decide which tool fits your engagement.
Whichever tool you choose, the principles in the Practical Guide still apply: AI assists, the auditor decides. Keep identifiable client data off prompts that go to consumer tiers. Document AI use under SA 230. Verify every citation.
For official AI credentials and CPE-eligible programmes, refer to ICAI’s AI portal — AICA Level 1, AURA, and the AI Innovation Summit. CORAA AI Lab is a free practice environment, not a regulator substitute.
