Last updated on June 3rd, 2025 at 12:42 pm
Have you ever wondered why some generative AI models sound biased, hallucinate, or produce weird responses?
If you’ve worked with or even just used a generative AI model, you’ve probably felt that moment of doubt: Can I trust this output? If that question has crossed your mind, you’re not alone.
Whether you’re building generative AI models for language or business automation, the challenges are the same: bias, reliability, hallucinations, and data leaks. These are real issues. For managers or tech leads, the fear of rolling out something that damages the reputation or misinforms users is just as real.
Why Are You Building Generative AI Models?
Generative artificial intelligence (Generative AI, GenAI, or GAI) is a branch of AI that creates text, videos, images, or other types of data using generative models.
Before jumping into datasets or tools, ask yourself: what’s the primary goal of a generative AI model?
Is it to:
- Automate customer support with natural replies.
- Generate content or code.
- Summarise reports and meetings.
Clear purpose gives you direction. A generative AI model without a well-defined goal ends up doing everything and nothing well.
When your objective is set, you can make smarter choices about data, model size, and deployment.
Choose the Right Data: Quality Matters More Than Quantity
Not all data is good data. And biased data leads to biased AI.
Here’s what you should look for:
- Diversity: Represent all user types in different regions, languages, and demographics.
- Cleanliness: Remove noise, duplicates, and outdated info.
- Context: For generative AI models for language, maintaining tone, clarity, and structure is key.
The model will only be as smart as the data you feed it. This is where many teams go wrong. They train on large datasets without checking data quality.
Architecture Choices: Not Just Transformers
The tech stack is important, but it shouldn’t be trendy for the sake of it.
Depending on your task:
- Use GPT-style transformers for natural text.
- Try diffusion models for image generation.
- Apply BERT-like encoders for classification + generation hybrids.
Think beyond OpenAI and Hugging Face. There are other options like Meta’s LLaMA, Google’s PaLM, or even custom-trained smaller models if cost is a concern.
Choosing the right architecture also helps control hallucinations especially in generative AI models for language.
Training the Model: Don’t Skip Human Feedback
Training isn’t just pushing data through epochs. Use a combination of:
- Supervised learning to teach patterns.
- Reinforcement learning with human feedback (RLHF) to refine outputs.
If you’re skipping human feedback because of budget, understand this: it’s the difference between a tool your team can rely on and one they’ll abandon.
During training, monitor loss values, watch for overfitting, and validate on unbiased test sets. This builds model trust brick by brick.
Where Things Go Wrong in Generative AI Projects
| Problem | What Causes It | How to Prevent It |
|---|---|---|
| Hallucination | Poor training data, no RLHF | Use curated data + human review |
| Bias in output | Imbalanced dataset | Diversify data sources |
| Repetition or gibberish | Poor architecture settings | Tune decoding strategies (Top-K, Temp) |
| Privacy issues | Training on sensitive/private content | Anonymise and sanitise input datasets |
| Poor context understanding | The model is not fine-tuned for the task | Task-specific fine-tuning |
This table can help identify issues early before deployment damages user trust.
Generative AI is growing quickly and brings powerful solutions to many industries. You can use it to build strong, innovative tools tailored to your sector, helping you stay ahead of your competitors.
The generative AI market can reach US$1.18 billion in 2025. Between 2025 and 2031, it is projected to grow at an annual rate of 37.01%, with the market size estimated to hit US$7.81 billion by 2031.
Here are some key areas:
Testing the AI: Don’t Just Test—Stress It
Testing is where most confidence gets built.
Don’t just test for correct outputs.
Test like:
- A user who types nonsense.
- A customer who speaks Hinglish.
- An angry client who repeats the same query 4 times.
Build evaluation checklists around:
- Bias and fairness
- Relevance of output
- Stability across different prompts
Even the primary goal of generative AI model is incomplete if you ignore testing.
Ethics, Governance, and Human Control
Even the smartest generative AI model is still just a tool. It needs guardrails.
Set up:
- Prompt filters to avoid toxic content
- Output moderation
- Human-in-the-loop for sensitive decisions
Also, document your AI decisions. This builds accountability. If something goes wrong, you’ll know how it went wrong.
Remember, building trust isn’t just about tech. It’s about control and governance, too.
Post-Deployment: Monitor Like You Mean It
Once the model is live, the real job begins.
Watch:
- Output logs for odd patterns
- Feedback loops (thumbs up/down)
- Changes in user engagement or satisfaction
Retrain based on what you learn. Generative AI isn’t fire-and-forget. It’s build, learn, improve, repeat.
Generative AI Course for Managers in Association with PwC Academy and Imarticus Learning
The Generative AI for Managers course by Imarticus Learning, in partnership with PwC Academy, is for professionals who want to not just use but lead with AI.
This 4-month generative AI course includes live online weekend sessions perfect for working managers. It blends real-world problem-solving with industry-led case studies from sectors like finance, marketing, and operations.
You’ll gain hands-on experience on how to tackle business challenges using proven AI methods. This includes practical strategies, team applications, and even how to communicate AI impact with stakeholders.
By the end of the Generative AI course for Managers, you’ll not just understand AI; you’ll use it with purpose and clarity in your organisation.
Join the Generative AI for Managers programme today and move from trial-and-error to trained impact.
FAQ
What are generative AI models used for?
Generative AI models create content like text, images, & audio based on the data they’re trained on.
What is the primary goal of generative AI model?
The goal is to generate new, relevant content that resembles the training data. This includes language generation, automation, and personalisation.
What are generative AI models for language?
These models generate human-like text for tasks like summarisation, chatbots, translation, and content creation.
Can I trust generative AI models for business use?
Only if they’re built with bias testing, human feedback, and continuous monitoring, trust comes from how they’re trained and governed.
Do generative AI models replace human workers?
Not really. They support humans in decision-making, content production, and data analysis, but human oversight remains essential.
Is there any risk of generative AI producing fake information?
Yes, hallucinations can happen if data isn’t clean or if the model isn’t fine-tuned. That’s why testing and monitoring are vital.How can I start building trustworthy generative AI models?
Start with clear goals, diverse data, ethical design, and regular feedback. Then, iterate based on user interaction and output quality.