For many, ChatGPT has become the default AI assistant for many teams, but can you really trust it with contracts, compliance, multilingual content, translations, and critical decisions? We explore its strengths, risks, and where it truly fits in an enterprise stack.

What do we mean by factual accuracy for ChatGPT, and why is translation accuracy relevant?

For enterprise ChatGPT use, factual accuracy is a six-part framework including fidelity, factual correctness, determinism, terminology, risk-adjustment, and auditability. With traditional machine translation systems, accuracy usually means fidelity: how close the target sentence is to the source, as evaluated by a human or a group of evaluators. For a general-purpose Large Language Model like OpenAI’s ChatGPT, "accuracy" is broader and has a more complex definition.

In an enterprise or government context, "accuracy" typically includes:

• Fidelity to the source: preserving all facts, conditions, numbers, and references.
• Factual correctness: avoiding plausible but false statements or hallucinations.
• Consistency and determinism: producing stable answers for similar inputs.
• Terminology and style: adhering to brand language, legal formulas, and technical terms.
• Risk-adjusted accuracy: understanding whether a small error would have significant consequences.
• Governance and auditability: being able to trace what was generated, when, and under which controls.

When we ask how accurate ChatGPT is for business, we are really asking whether it meets all of these criteria for each specific use case, not just whether the text "sounds good". So, the question remains: What is the real accuracy of ChatGPT vs. traditional machine learning and machine translation?

For ChatGPT and similar LLMs, accuracy is a multi-dimensional risk framework, not the linguistic score that humans (mathematicians, lawyers, translators, proofreaders, editors, etc.) have traditionally used. For example, while traditional Machine Translation (MT) measures fluency and fidelity against a reference (using metrics like BLEU and METEOR), ChatGPT’s accuracy hinges on factual correctness, consistency, and the critical mitigation of hallucinations; that is, confidently generated false information. In business, fluency is a given; the core challenge is ensuring outputs are reliable, verifiable, and safe for critical use cases like contracts, compliance, and multilingual content with controlled terminology and style (something that was pretty direct in neural machine translation systems). Actually, both LLM and NMT are neural MT; they use the same Transformers, but they use them in different ways.

MT has always measured with metrics, but LLM uses “risk frameworks”

Fun Fact: Did you know that many LLM scientists like Ilya Sutskever (ex OpenAI CTO) or Adian Gomez (Cohere CEO) began their days in machine translation? Gomez was part of the Google Translate team. Sutskever wrote and co-wrote several papers on machine translation using Transformers, and encoder-decoder technologies before joining OpenAI. This reflects how closely related both technologies are.

The definition of “accuracy” has fundamentally evolved from traditional systems to general-purpose Large Language Models (LLMs).

For example, in traditional MT, the goal was (and still is) to measure linguistic fidelity. Let us not forget that several key teams and people in LLM development and AI companies spent their formative years solving translation as a first AI challenge. Traditional machine translation systems were evaluated on how closely their output matched human reference translations. This was measured with automated metrics:

Metric	What It Measures	What it means for enterprises
BLEU	Precision of word/phrase matches (n-grams) to reference.	Penalizes valid rephrasing; ignores meaning and factual correctness.
METEOR	Matches based on exact form, stems, synonyms, & sentence structure.	More aligned with human judgment than BLEU, but still focuses on form over factual truth.

These systems treated fluency as the goal to be achieved. The primary task was linguistic transformation with high fidelity.

Modern LLMs (ChatGPT): A multi-dimensional accuracy challenge

For a Generative AI like ChatGPT, “sounding good” is a baseline capability. Enterprise accuracy is a broader, more complex requirement that includes:

• Factual correctness & hallucination avoidance: The model must generate information that is true and verifiable, not plausible fabrications.
• Determinism & consistency: Similar inputs should yield stable, repeatable outputs, a challenge for probabilistic AI.
• Governance & auditability: Organizations must trace how an output was generated, under which controls, and on what data.
• Risk-adjusted accuracy: Understanding if a small error (e.g., in a number, date, or clause) could have significant legal, financial, or reputational consequences.

Here, fluency is a given, but hallucinations are the paramount concern.

Why AI hallucinations are the top enterprise risk (for facts and translation)

Hallucinations are not typos or grammatical slips. Returning to the parallel in translation systems, we have grown accustomed to seeing fluency issues in language translation over the years. There are jokes and even books about naive uses of machine translation. Hallucinations are a different kettle of fish: they are confidently stated fabrications that are seamlessly woven into otherwise fluent and coherent text. And we know this from our first interactions with the first LLM-based chatbots (ChatGPT 3.5 onwards). It is an inherent trait of the technology, pairing related data points in a vector space. This makes them exceptionally dangerous for business use and specific applications (and let’s not forget the privacy point of sending private, confidential, and/or government data). What are the risks of using a "large external brain"?

• Legal and compliance risks: Hallucinations can create fictitious legal clauses (happens often, even in the last ChatGPT versions 5+), regulatory precedents, or financial data. There are documented cases of AI generating entirely fabricated court citations, rendering legal submissions invalid.
• Reputational damage: Imagine the question... "Have you really published marketing content without human review?" "Did you really translate those client documents using ChatGPT and nobody proofread them? Why was the third clause summarized, and 7.1 missing?" Imagine sending unrelated support responses or reports containing confident falsehoods that can destroy trust in your brand (or your own job).
• Operational failure: Strategic decisions, financial forecasts, or technical instructions based on hallucinated information lead directly to monetary loss.

Progress and persistent gaps: According to a September 2025 OpenAI technical report, GPT-5 is reported to have made strides with a six-fold reduction in hallucinations for sensitive topics, but the risk is structural. Hallucinations cannot be fully “solved” in a probabilistic model, only managed. This, together with a growing understanding that developing or fine-tuning task-specific small language models pays off (or domain-specific small language models, as Gartner puts it), token costs, and greater concerns about explainability, is driving organizations to seek help in building or fine-tuning small models for their specific use cases that they tend to host. The EU, for example, has several projects dedicated to public administrations adopting fine-tuned small models, in which some members of our staff have served as evaluators. The US federal government has also begun a series of calls to deploy on-device, private AI for government agencies.

In Europe, the initiative to scale and replicate Generative Artificial Intelligence (GenAI) solutions across EU public administrations is a comprehensive and strategic effort aimed at enhancing efficiency and innovation in the public sector. By developing tools such as starter kits and replicability assessments, the initiative provides a framework for public administrations to adopt and adapt successful GenAI solutions. This approach not only saves time and resources but also ensures consistency and effectiveness in implementing AI technologies across different regions and sectors.

The initiative also emphasizes collaboration between public administrations and startups, fostering a culture of innovation and practical problem-solving. Through outreach and awareness-raising activities, the initiative educates public officials about the benefits of GenAI and encourages wider adoption. By integrating with broader European AI initiatives and platforms, the public sector can leverage shared knowledge and resources, further enhancing the impact of GenAI technologies. Ultimately, this initiative aims to create a sustainable, collaborative community of practice that drives the adoption of GenAI and improves public service delivery across Europe.

The EU Calls for AI adoption of task-specific small models for Public Administrations, 2025 and 2026

Actionable strategies: Mitigating hallucination risks in business applications

While our personal experience suggests reasonable results when using ChatGPT for small, individual tasks that we inevitably review, enterprises cannot rely on a general model alone. Safety must be engineered through process and technology. For example, following these steps:

1. Technical mitigations to ground outputs.

• Implement Retrieval-Augmented Generation (RAG): This is the most effective technical guardrail. RAG grounds ChatGPT’s responses in your own curated databases (knowledge bases, document repositories) by retrieving relevant facts before generating an answer, drastically reducing reliance on the model’s internal knowledge. However, please note that you are using a vector database to correct a probabilistic model. Studies show that although RAG reduces the risk of hallucinations, it is not an infallible solution.
• Advanced Prompt Engineering: Use system prompts to constrain behavior. However, this depends largely on the user’s skill.
• Direct instructions like: “Base your answer solely on the provided context. If the answer is not in the context, state ‘I cannot answer based on the provided information.’” can reduce off-context fabrication.

2. Essential human and process guardrails

• Mandate Human-in-the-Loop (HITL): For any high-stakes output (legal drafts, compliance reports, public communications), institute a mandatory review by a subject matter expert before use. Human experts are the ultimate safety net.
• Develop Clear AI Use Policies: Explicitly define approved, restricted, and prohibited use cases. For example, prohibit autonomous use for final contract generation or unvetted financial advice, while allowing it for brainstorming or drafting internal summaries.

2. Governance, measurement, and continuous improvement

• Adopt an AI Governance Framework: Involve Legal, Compliance, and Internal Audit teams to establish controls covering data privacy, security, output validation, and ethics.
• Track Quality and Impact Metrics: Move beyond basic usage stats. Measure hallucination incidence in production, user correction rates, and the impact on workflow efficiency and error reduction.

Key recommendations for ChatGPT-friendly implementation

To integrate ChatGPT accurately and safely into your enterprise stack:

• Start with policy, don’t start with pilots: Before rollout, establish a formal "Acceptable Use Policy" that identifies high-risk domains and mandates human oversight.

• Prioritize grounding techniques: For specialized tasks (multilingual content, technical documentation), develop or partner with a developer like Pangeanic for Private GenAI to invest in custom-made RAG architecture to tether ChatGPT to your single source of truth.

• Adopt a “verify, then trust” mindset: Treat all AI-generated content as a high-quality first draft. For facts, figures, legal terms, and references, always corroborate with primary sources.

• Educate your team: Train all users on the capabilities and (critically) the limitations and risks of Generative AI, focusing on hallucination recognition. By framing accuracy as a comprehensive risk-management challenge, enterprises can harness ChatGPT’s transformative potential while safeguarding against the unique and significant risks posed by AI hallucinations.

What independent studies say about ChatGPT’s accuracy

Since 2023, researchers and practitioners have compared ChatGPT-style models to both classical machine learning, machine translation and other enterprise tools. The overall picture is nuanced. LLM-based chatbots are:

Strong on nuance and readability

ChatGPT is often praised for producing texts that read naturally and handle subtle context well. It can infer implied subjects, connect ideas across sentences, and adapt tone for different audiences. For marketing or narrative content, this can be a real advantage.

Mixed fidelity and terminology stability

On the other hand, specialist engines such as domain-adapted NMT or tools like DeepL tend to be more stable when strict, sentence-by-sentence fidelity is required in translation. For factual information, only custom-built RAG (or alternative) systems can follow strict guidelines. ChatGPT sometimes merges sentences, changes the order of items as it thinks something is more relevant, omits minor details, or reformulates in a way that slightly shifts emphasis. That is acceptable for low-stakes content, but not for contracts or regulated documentation.

Prompt and context sensitivity

ChatGPT’s performance is highly susceptible to the prompt and the amount of context provided. Clear instructions, examples, and enough surrounding text can significantly improve quality. However, this also means that small changes to templates or context windows can change accuracy in unpredictable ways if not carefully governed.

Hallucinations as a structural risk

Across domains, hallucinations remain a central concern. ChatGPT can invent sources, regulations, or seemingly precise figures. When employees ask it for legal, medical, or compliance advice, this behavio is not just a quality issue but a governance problem.

You use task-specific tools for specific jobs, and the same applies for ChatGPT, which is an external generalist, not task-specific.

Where ChatGPT shines for business tasks

Despite these risks, there are important areas where ChatGPT is genuinely helpful and accurate enough when used with the right guardrails.

Complex summaries and signal extraction

ChatGPT is very strong at summari long threads of emails, tickets or reports, extracting key decisions, risks and stakeholder positions. In these tasks, capturing the overall meaning matters more than exact phrasing, and the model often provides clear value.

Drafting and re-drafting content

For marketing, internal communications, or training materials, ChatGPT can produce strong first drafts and adapt tone for different audiences or locales. Human review is still necessary, but it can accelerate content creation significantly.

Multilingual understanding and exploratory translation

ChatGPT is useful as a multilingual assistant: it helps staff read foreign language content, compare alternative phrasings, and reason about meaning across languages. Even if you rely on specializ MT for production translation, this exploratory layer can improve decision-making.

Where ChatGPT is risky or not accurate enough on its own

High-stakes domains and hallucinations

In legal, medical, financial, and compliance contexts, hallucinations are unacceptable. ChatGPT can confidently generate explanations and recommendations that lack reliable sources. Without grounding and review, this can lead to incorrect decisions and regulatory exposure.

Data privacy, compliance, and sovereignty

There is a crucial distinction between public ChatGPT and enterprise offerings with stronger privacy guarantees. Even then, organizaions must define clear policies around what data can be sent to external models and when on-premises or sovereign deployments (private GenAI) are required.

Instability and reproducibility

Because outputs can vary with temperature, model version and prompt phrasing, ChatGPT is not inherently deterministic. For audited, repeatable workflows, this needs to be mitigated with standard prompts, strict configuration, logging and human oversight.

ChatGPT vs specializ tools: not a binary choice

It is tempting to frame decisions as ChatGPT versus enterprise software, DeepL, specific algorithms and developments, or classic Neural Machine Translation. In practice, most organizations will end up with a hybrid architecture where different engines handle different parts of the workflow.

Specialist engines remain best for narrowly defined, high-volume tasks such as technical documentation translation, speech recognition, or search across known corpora. ChatGPT-style models excel in open-ended reasoning and language manipulation: summarizing, restructuring, adapting tone, and synthesizing information across sources.

The fundamental design question becomes: how can we orchestrate all of these engines within a single, governed layer, rather than letting staff interact with each tool in an ad hoc way?

The Pangeanic approach: Taming LLM unpredictability

At Pangeanic, we treat ChatGPT and similar models as powerful components inside a broader LLM and Translation Hub, adding our Deep Adaptive AI Translation stack, not as standalone solutions.

Routing content to the right engine

Our LLM Hub and Translation Hub routes requests according to the best specific task, language pair, domain, and risk level: generic or low-risk content can use public or multi-tenant models, while high-risk domains are handled by domain-adapted NMT, smaller task-specific models, and on-premises deployments.

Grounding LLMs in your own data

We combine retrieval augmented generation with your data for custom, on-premises deployments in knowledge retrieval, your translation memories, terminology, style guides, and approved documents for custom MT results. This reduces hallucinations, enforces terminology, and keeps outputs aligned with your existing assets.

Making accuracy an asset you own

Feedback from human users of our ECOChat or human post-editors, internal reviewers, and automated quality estimation flows back into the system. Over time, smaller models and MT engines become more aligned with your organization, rather than relying solely on public model roadmaps.

How to evaluate ChatGPT in your own organization

If ChatGPT is already being used informally, the key is not to ban it but to bring structure and evidence to its use. A simple evaluation framework can help.

1. Map use cases and risk

List concrete scenarios where ChatGPT is used or could be used, from summar tickets to drafting emails or translating documents. For each, assign a risk level based on regulatory exposure and business impact.

2. Build representative test sets

For the most important use cases, collect small sets of real examples and define what a good output looks like. Anonymize content where needed.

3. Compare ChatGPT with existing tools

Run the same test sets through ChatGPT and your current tools, such as internal search, OpenSearch databases, NMT engines or human workflows. Keep prompts and settings stable so that results are comparable.

4. Score quality, risk, and effort

Ask domain experts to rate outputs on fidelity, factual correctness, terminology, style, and required post editing effort. Use simple scales and document examples of both good and problematic behavio.

5. Decide where ChatGPT fits

Use these results to define green zones where ChatGPT is allowed with light review, amber zones where it can only be used inside governed tools, and red zones where specializ engines or human workflows are required.

Frequently Asked Questions (FAQ)

Is ChatGPT accurate enough for enterprise use?

ChatGPT is accurate enough for many low-risk tasks, such as drafting, summari, and exploratory translation, especially when prompts and context are well-designed. For high-stakes or regulated content, it should only be used inside governed workflows and combined with specialist engines and human review.

What are the main accuracy risks when using ChatGPT in a business context?

The major risks are hallucinations, subtle shifts in meaning, unstable terminology, and a lack of traceability to authoritative sources. These risks increase with domain complexity and regulatory pressure, making unmanaged use of ChatGPT problematic for legal, medical or compliance content.

Can ChatGPT replace specialist tools like machine translation engines or search?

No. ChatGPT complements but does not replace specialist tools. Domain-adapted MT engines, ASR, and enterprise search still offer better control, determinism, and integration for narrow tasks. ChatGPT provides value as a reasoning and language layer on top of those systems.

How should enterprises evaluate ChatGPT accuracy for their workflows?

Enterprises should evaluate ChatGPT on real content by building small test sets for each key use case, comparing outputs with existing tools, and asking experts to rate quality, risk, and required human effort. Governance decisions should be based on this evidence, not on ad hoc experiments.

Is it safe to send confidential data to ChatGPT?

Confidential or regulated data should never be sent to public ChatGPT. Even with enterprise offerings that provide better privacy guarantees, organizaions must define clear policies, technical controls, and logging. In many cases, on-premises or sovereign deployments are required for sensitive flows.

Where does ChatGPT fit in a hybrid enterprise translation and content stack?

ChatGPT fits best as a component within a governed platform, such as Pangeanic’s Translation Hub, where it can handle summarization and re-drafting. In contrast, domain-adapted MT and smaller models handle high-volume and high-risk translation. This hybrid approach offers both innovation and control.

Talk to Pangeanic about a hybrid, governed AI stack or private AI models

If you want to move from ad hoc use of public AI tools to a secure, enterprise-grade multilingual stack, our team can help you design a roadmap that fits your risk profile, technology landscape, and business goals.

Contact Pangeanic