Strengths of AI Buddy – much more than natural language processing
Data in its various formats and massive amounts is stored and processed by virtually all organizations. Tools, standards, and methodologies for making usage of this data are subject of constant development in recent years. The promise to have “data driven decisions,” “data products” or “data economy” is still alive and still fuels the minds of many to find the solution that fits given business best. The data (r)evolution is on, and recently new techniques have been developed that was not seen before – generative AI.
The introduction of Large Language Models gives an opportunity to use new methods to manage access and use all available data. In the past many organizations strived to create vertical models with neural networks and machine learning for several use cases. Now, as we have big language models, we can use them is several ways to get things done with our own data:
• Use natural queries in any language to ask about insights and facts from your data.
• See and examine how problems are being solved by AI with proposed chain of thoughts and actions taken.
• Ask for charts, diagrams, summaries or simply ask a question.
• Be aware that the tool is using all available data including internal or public, structured, or unstructured in a secure way.
LLMs (Large Language Models) like ChatGPT are used to understand a problem and provide sequence of actions for getting a solution („chain of thoughts”). This process, done by specialized prompting, is transparent so people can see how the result is generated and assess its correctness. AI Buddy uses Retrieval Augmented Generation Approach (RAG) to fine tune Large Language Models with relevant data.
Data and information provided by AI Buddy tools is up-to-date and exact and can also include internal information. A tool provides requested information from a database, warehouse, public data, or any API (Application Programming Interfaces) needed in the context of AI Buddy. It can be internal and public data that are static or changing frequently like currency rates, weather forecasts, transaction data etc. We make sure that tools are safe and respect all security policies. This approach eliminates the hallucination effect since if tools are not providing relevant info for given problem, then application can respond „I have no data about it.” To do so prompts are engineered and fine-tuned to give exact and relevant information only.
Manager and Senior Consultant at Tietoevry Create, Data Practice
Key benefits
Use automated data analytics with genAI
Large Language Models deliver capability to answer questions in any natural language, solve problems and write code. It can be used analyze provided data and create new insights - all by AI!
Keep your data safe
While we need to push the data to LLMs to be analyzed, we can control the whole process. Only required data is being sent and we can provide advanced processing like anonymization whenever required.
Get only reliable results
We fine tune LLMs with prompt engineering to eliminate hallucination effect and force genAI to use data only in given context. We can also use multiple models to compare and verify results.
In the spotlight
ChatGPT and Deepfakes
As we become accustomed to living with AI products, ugly aspects start to appear. What tools can help us make AI models more trustworthy?
Learn moreHow can we use generative AI to make our work more efficient and our lives easier and more fun?
Lukas Lundin from Microsoft visited our Data Insiders podcast to share his AI predictions and prompt engineering tips.
Listen nowImproving response reliability for Large Language Models
How Retrieval-Augmented Generation can revolutionize the way we access information
Find out how Large Language Models can unlocking the power of LLM servicesEager to start talking with AI Buddy?
Let us find a use case
Use case discovery workshop is a great idea. The optimal approach for the first use case is to narrow its scope and concentrate on addressing specific issues within a single domain. It can be Sales Analytics, Customer Care, Insurance Agent, Knowledge Broker or similar.
Let us identify relevant data sources
Although we rely on LLM’s capabilities, and we let AI drive conversation and problem solving we need to give it right data. Without it even AI of the future will not give correct answers. Let us plug-in Google search, API to your cloud systems or internal sources or any other relevant sources.
Prototyping!
Let us create something working and assess early results. We can pick one or another LLM of your choice and fine-tune prompts to get the best possible results.
Final details
Our consulting team can guide you about the costs using LLMs, security measures used, data privacy and all relevant issues before starting production roll-out. And then AI Buddy will be incarnated in your organization within weeks. You can even give it a name of your choice if you like. Contact us if you need such an AI Buddy!
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