AI chatbots are everywhere, but many fail to deliver. Building AI that truly works is complex. They hallucinate, give incorrect answers, or frustrate customers, ultimately damaging your brand. So, how do you build a chatbot that is genuinely helpful, accurate, and reliable?
The answer isn’t just about using a powerful AI model; it’s about disciplined engineering. At OpenArc, we use a data-driven, test-first methodology to ensure the AI solutions we are building deliver measurable quality and a clear return on investment.
Here’s a look at our process that defines how building AI should be approached.
Step 1: Defining Success Before We Start
Before writing a single line of code, we define what a “good” answer looks like. We collaborate with you to build a comprehensive test framework using specialized tools like deep-eval. This framework might contain, for example, your real-world Frequently Asked Questions (FAQs) and the approved, correct answers.
Why this matters for you: This establishes a concrete, objective benchmark for quality from day one. You’ll know exactly how we measure success when building AI chat systems.
Step 2: The Baseline Test — What Can a Generic AI Do?
Next, we test a powerful, general-purpose Large Language Model (LLM)—think of it as a highly intelligent brain with no specific knowledge of your business—against our test framework. This gives us a baseline performance score. Unsurprisingly, while the answers might be well-written, they often lack specific company context and accuracy.
Why this matters for you: This “before” snapshot clearly demonstrates the limitations of an out-of-the-box AI. It quantifies the performance gap we need to close when building AI tailored to your needs.
Step 3: The RAG Enhancement — Giving the AI Your Company’s Brain
This is where the magic happens. We implement a Retrieval-Augmented Generation (RAG) system.
In simple terms, we give the AI an “open-book test” where the only book it can use is your approved knowledge base—your FAQs, product manuals, and internal documentation. The AI is now required to find the correct information from your documents before formulating an answer.
We then run the exact same tests again. The improvement is immediate and dramatic.
Why this matters for you: We can now prove the value of connecting the AI to your proprietary data. The quality score jumps, and we have a hard number that shows how much more accurate and reliable the chatbot has become following our building AI strategy.
Step 4: Guiding Investment with Data, Not Guesses
As your consulting partner, our goal is to help you make smart decisions. The test scores allow us to have a strategic conversation:
- Is the 92% accuracy achieved with RAG “good enough” for your initial launch?
- Or, for a critical customer-facing function, is it worth investing in further fine-tuning to reach 98%?
Why this matters for you: This process de-risks your investment. You can make informed, budget-conscious decisions based on objective data, ensuring the final product aligns perfectly with your business goals and quality standards.
Our Commitment: Continuous Quality Measurement
Our work doesn’t stop at launch. Throughout our engagement, we continuously re-run these tests with every new change or software sprint. This allows us to show consistent progress and, just as importantly, catch any “regressions” where a new feature might have accidentally broken something that was working before.
This commitment to measurable quality is a cornerstone of our approach. It’s how we move beyond the AI hype to build robust, reliable applications that create lasting value for your business when building AI solutions.
