In early 2025, an AI model from China shook the global technology market. DeepSeek R1 was launched with performance said to be on par with GPT-4 but with significantly lower development costs, and most surprisingly: available as open source. A few weeks later, Qwen from Alibaba followed suit with impressive multimodal capabilities. The era of democratic AI has begun.
For ordinary users such as students, researchers, and office professionals, this is fantastic news. High-level AI is no longer locked behind expensive subscriptions or enterprise infrastructure. All that is needed is an understanding of how to use it effectively.
DeepSeek vs Qwen: Choosing the Right Tool
DeepSeek and Qwen are not identical products; each has different strengths suited for different use cases. DeepSeek, particularly the R1 series, is known for its strength in complex reasoning and multi-step analysis. This model is highly effective for tasks that require step-by-step reasoning such as data analysis, code debugging, and academic problem-solving.
Qwen from Alibaba excels in multimodal capabilities and long-context understanding. Its strong document analysis, image processing, and table comprehension make Qwen an ideal choice for tasks involving complex documents, spreadsheets, or visual content.
Meanwhile, Ollama allows both models to be run locally on your own computer without your data being sent to external servers. For organizations sensitive to data privacy, this is a very important consideration.
Prompting Techniques That Change Outcomes
Using AI without the right prompting techniques is like having a sophisticated calculator but only knowing how to press number buttons. The results fall far below its true potential.
Three proven effective prompting frameworks: RTF (Role, Task, Format) asks the AI to take on a specific role, perform a clear task, and produce output in a specified format. TAG (Task, Action, Goal) is suitable for result-oriented tasks with explicit steps. RACE (Role, Action, Context, Expectation) is most effective for complex prompts that require contextual nuances.
- Custom instructions configure the AI to always respond according to your specific profile and preferences without needing to explain again each session.
- Chain-of-thought prompting asks the AI to explain its reasoning steps, which increases accuracy for complex tasks.
- Few-shot examples provide examples of desired output to train the AI to produce consistent formats.
AI for Academic Research: Not Cheating, But Efficiency
One area where AI has the greatest impact is academic research. Processes that previously took days can now be dramatically shortened, but it is important to understand the right way to do so without compromising academic integrity.
AI can assist in searching and filtering relevant journals from large databases, help understand research methodologies in more digestible language, analyze citation patterns to map the research landscape of a topic, and assist in paraphrasing to enhance the originality of expression without altering the substance of ideas.
What should not be done: asking AI to write scientific arguments without verification, using AI output without cross-checking with primary sources, or claiming AI analysis results as your own original thoughts. AI is a research assistant, not a substitute for critical thinking.
Office Productivity: From Meetings to Presentations
For office professionals, AI opens up automation opportunities that were previously only available in large companies with big IT budgets. Automatic meeting transcription, structured minutes creation, draft reports from raw data—all of this can now be done in minutes.
AI can also generate draft presentation slides from text outlines, create diagrams and flowcharts from verbal descriptions, and analyze spreadsheets to find relevant patterns and anomalies. The time saved from these administrative tasks can be redirected to work that requires human judgment, strategy, creativity, and interpersonal relationships.
Leveraging AI for Investment Analysis
One increasingly popular application of AI is stock and crypto market analysis. With the right prompting techniques, AI can help synthesize financial reports, identify technical trends from historical data, and compare performance across assets—information that previously took hours to gather manually.
It is important to note that AI is not a crystal ball; no AI model can predict market movements with perfect accuracy. However, as a tool to accelerate research and assist in more informed decision-making, AI provides a real advantage for individual investors.
Start Mastering AI Before AI Replaces You
That sentence sounds dramatic, but it is the reality being shaped. It is not AI that will replace human jobs, but rather humans who are skilled at using AI who will replace those who are not. This competency gap is wide open, and those who fill it early will have a significant competitive advantage.
References:
- DeepSeek – DeepSeek-V2: Technical Report → deepseek.com
- Alibaba Cloud – Qwen: Open Large Language Model Series → qwenlm.github.io
- Stanford HAI – AI Index Report: Open-Source AI → hai.stanford.edu