Enhance your application with AI features: chatbots, summaries and recommendations

Artificial intelligence is becoming increasingly present in modern software systems. Many professionals interact with AI-driven functionality on a daily basis, often without being fully aware of it. As AI capabilities continue to advance, integrating them into applications is becoming both more accessible and more relevant.

If you are considering improving an existing application, or designing a new one, by incorporating features such as automated summarisation, recommendation engines or conversational interfaces, this article provides an overview of the key aspects to take into account.

Why AI matters

The integration of AI into existing software systems is no longer a temporary trend. It is a strategic decision that can significantly improve how users interact with digital products and how teams operate internally. Although the adoption of AI introduces several technical and organisational challenges, the long-term advantages generally outweigh the initial effort. AI enables more efficient, adaptive and reliable application behaviour, which can contribute to sustained product value and user engagement.

Reasons to add AI to existing projects

• Improved user experience: AI enables personalized and context-aware features that adapt to individual user needs.
• Automation of repetitive tasks: Routine or operational tasks can be handled by automated workflows, reducing manual workload and operational risks.
• Data-driven decision-making: AI systems help interpret large volumes of data, resulting in more accurate insights and outcomes.
• Stronger competitive position: Integrating AI prepares products for future requirements and helps maintain relevance in fast-developing markets.

Challenges to consider

• Data quality requirements: AI models depend on accurate, consistent and well-structured data.
• Integration complexity: Adding AI to existing architectures may require significant adjustments, especially when systems were not initially designed for it.
• Initial investment: Setting up and training AI models often involves considerable resource allocation.
• Privacy and ethical considerations: Data handling must comply with legal and ethical standards.
• Ongoing maintenance: AI models require continuous optimisation, monitoring and updates to remain effective.

Planning your AI integration

A successful AI implementation begins with clear objectives and thorough preparation. The primary goal is to identify where AI provides meaningful value, rather than applying it to every component of a system.

Below are key stages to consider when planning AI integration:

• Identify high-impact use cases. Focus on areas where AI can demonstrably improve efficiency, accuracy or user satisfaction.
• Evaluate ROI and feasibility. Prioritise initiatives that deliver measurable outcomes and align with broader organisational goals.
• Decide between build and buy. Determine whether to develop custom models or adopt cloud-based AI services, based on internal expertise, available resources and timelines.
• Design for scalability and flexibility. Ensure the architecture supports modular, high-performance AI components that can be updated or replaced as technologies evolve.

AI is not a universal solution, and not every project requires it. When applied strategically, however, AI can strengthen systems by improving user experience, operational efficiency and long-term maintainability. The focus should be on identifying processes where AI can create tangible value and where objectives can be measured effectively.

Core AI features

The first step in integrating AI into a product is to focus on practical, user-facing capabilities. These features introduce intelligence into daily interactions and can improve usability, engagement and overall user experience. Key examples include the following components.

Chatbots and conversational interfaces

Chatbots and conversational interfaces enable more natural interactions by allowing users to communicate with an application through written or spoken language instead of relying solely on navigation. Modern AI-driven conversational systems rely on several core elements:

• Seamless integration: AI chatbots can be embedded into existing mobile or web applications to offer real-time assistance, user guidance and automated support without disrupting the current interface or workflows.
• Connection with large language models (LLMs): Integrating LLMs through APIs, such as OpenAI or Azure OpenAI, enhances reasoning capabilities, contextual understanding and the ability to respond to complex queries.
• Personalised conversations: Context awareness and session memory allow conversational systems to adapt to user preferences, historical interactions and intent, resulting in more relevant and consistent responses.

The choice of LLM depends on the specific requirements of the application. Common options include GPT-4o for high output quality, Claude 3 for use cases with stricter safety needs, LLaMA 3 for customisable open-source deployments and Mistral 7B for lightweight, high-speed performance.

Text summarisation

AI-based summarisation condenses large volumes of text into concise and informative output, supporting faster understanding and more efficient decision-making. Important considerations are:

• Application scenarios: Summarisation can be applied to long reports, emails, system logs, documentation and other text-heavy resources to accelerate information processing.
• Ease of implementation: Developers can integrate summarisation through pre-trained models or hosted APIs, requiring minimal infrastructure.
• Improved accuracy: Fine-tuning and contextual filtering help produce summaries that remain aligned with the original content, ensuring relevance and correctness.

Suitable LLMs depend on the use case. Qwen/Qwen2.5-72B-Instruct and gpt-4o-mini are strong candidates, with the former performing particularly well in recent benchmark evaluations.

Recommendation systems

Recommendation systems analyse user behaviour, preferences and interaction patterns to deliver personalised suggestions. They support user engagement, satisfaction and retention. Key points to consider include:

• Recommendation techniques: Modern systems commonly use collaborative filtering, content-based filtering or hybrid approaches to generate diverse and accurate recommendations.
• Integration into user workflows: Recommendations can be incorporated naturally into browsing, purchasing or content consumption pathways without interfering with the overall experience.
• Adaptive learning: Models refine their outputs continuously based on new user interactions, ensuring relevance over time.

There is no single optimal LLM for recommendation systems. The choice depends on performance expectations, operational constraints and available data. GPT-3.5, GPT-4 and Claude 2 are frequently used options. In many applications, LLMs serve as complementary components alongside traditional recommendation algorithms.

Testing, evaluation and next steps

To ensure that AI features deliver measurable value, organisations need to focus on thorough evaluation, continuous monitoring and deliberate planning for future scaling. These activities help transform initial implementations into reliable, long-term solutions.

A. Evaluation of AI performance and quality

The primary objective is to understand how well AI features operate and how they affect the user experience.

• Performance metrics: Track indicators such as precision, recall and accuracy to quantify system performance.
• User feedback: Assess satisfaction and engagement levels to evaluate real-world impact.

B. Monitoring and continuous improvement

AI models require ongoing refinement to remain accurate, relevant and fair.

• Usage tracking: Observing user interactions helps identify strengths, weaknesses and areas for improvement.
• Bias detection: Regular checks are necessary to detect and address potential bias and ensure equitable outcomes.
• Iterative improvements: Each iteration should focus on enhancing accuracy, personalisation and overall usability.

C. Scaling and future-proofing

Planning for scalability helps organisations maximise the long-term value of AI.

• Enhanced capabilities: Introduce new AI features or improve existing ones as product requirements evolve.
• Data growth: Ensure models can adapt to increasing volumes and complexity of user data.
• Sustainable architecture: Design systems that remain robust as demands on performance and complexity grow.

In summary, structured testing, ongoing monitoring and thoughtful scaling help maintain AI systems that are accurate, relevant and adaptable. These efforts support the transition from early experimentation to sustainable, long-lasting impact.

Final thoughts

AI is not intended to introduce additional complexity for end users. Its purpose is to simplify interactions by adding intelligence in areas where it provides clear value. When implemented thoughtfully, supported by a solid strategy and proper planning, integrating AI into a product can enhance its effectiveness and strengthen its competitive position.