Where It All Began

The inspiration for exploring this topic arose while developing a POC to generate accurate graphical reports and charts from quantitative data.

Naturally, the first thought was GPT. It’s everywhere—the “default” AI for almost any task. ChatGPT was given a try. It worked to an extent, displaying text-based charts or even generating HTML/Python code to create one. But here’s the catch: it wouldn’t provide the final chart image. Copying code elsewhere just to get the actual chart wasn’t the intended outcome.

Next came DALL·E. Since it’s also from OpenAI, perhaps it could do the job. Well… no. Beautiful, creative pictures? Absolutely. Structured data-based charts? Not really. What seemed like an easy win quickly turned into a mini quest.

Hunting for the Right Model

Several options went down the list—GPT-3.5, DALL·E, Gemini 1.5 Flash, DeepSeek R1. None produced the desired result.

Then Claude by Anthropic entered the picture, specifically Claude 4 Sonnet. Expectations were low, but the first attempt was a surprise: the chart came out clean, accurate, properly labeled—exactly what was needed.

Even with GPT-5 making waves, Claude remains the go-to choice for this type of work. Claude Opus 4 in particular shows a careful approach—checking dataset structure, avoiding mismatched axes, and even explaining why a certain type of chart is chosen.

Of course, with AI evolving so rapidly, a new model could take the lead at any moment.

The PDF Text Challenge

Another challenge involved extracting raw text from PDFs—word for word. Sounds simple, but GPT consistently summarized instead of returning the full text, and sometimes even cut sections short.

After multiple trials, Gemini 1.5 Flash proved to be the most effective, with around 95% accuracy in tests. The trade-off: large files had to be split into batches. DeepSeek R1 was too slow at the time and carried unresolved security issues, making it less viable.

This reinforced a key realization: every AI model has its own sweet spot. Choosing the right tool for the right task can save enormous frustration.

Why Agentic AI Works Better for Coding

For coding, “agent-style” tools often outperform general-purpose AI models. Cursor stands out as a strong option—it handles context with minimal explanation required, enabling focus on the bigger picture.

GitHub Copilot is solid, but Cursor integrates more naturally into certain workflows. While GPT and others can assist with code generation, Cursor feels more like an assistant who already understands the context.

A Favourite GenAI Win – Automated Reporting

One of the biggest successes with GenAI came in automating a reporting process that previously consumed hours or even days.

Here’s the approach:

• Train on Domain Data – Past global reports were provided as context.
• Pick Examples – From that set, the top 3 relevant examples were identified.
• Add Custom KPIs – KPI values from the user were integrated.
• Generate the Report – The AI produced a tailored report, charts included.

The result? Reports were delivered in seconds instead of days, with noticeable improvements in quality.

A key lesson emerged: prompt order matters. Change the sequence, and results can shift dramatically. For precise output, it’s critical to specify exact requirements, ideally in the final line of the prompt.

Quick Recap – Model Strengths

From these experiments, here’s how the strengths stack up:

• GPT-5 / GPT-4 – Excellent all-rounders, strong at reasoning and structured content.
• Claude Opus 4 – Best choice for structured data and accurate visualizations.
• Gemini 1.5 Flash – Reliable for extracting large chunks of plain text.
• DALL·E – Brilliant for creative/artistic imagery, less so for data visuals.
• DeepSeek R1 – Still maturing; slower and less stable in testing.

Wrapping Up

The key takeaway — no single AI model is the best at everything. Each has a niche.

• Need charts from data? Use Claude.
• Need full text from PDFs? Gemini.
• Want creative images? DALL·E.
• Need coding help? Cursor.

The AI landscape shifts quickly, so any list like this may become outdated soon. But the principle holds—don’t chase the trend, pick the tool that works for the task at hand.

That’s when GenAI moves beyond “cool tech” and becomes a true productivity booster.