We have seen evolution of product development from traditional coding to data-driven machine learning. Today, generative AI is capable of generating both data and code. This shift is set to transform product business models and how software AI driven product will be built. This will change product business model and also product management.
Generative AI is rapidly expanding its influence across industries, reshaping how creativity, business, and education intersect. A new project is using AI to restore missing scenes from a 1940s Orson Welles film, showing how the technology can revive lost art. In higher education, Duke University is testing custom AI tools to weigh both the opportunities and risks of AI-assisted learning. Across Europe, companies are adopting generative AI to simplify and update aging mainframe systems while ensuring compliance with strict regulations. Businesses are also beginning to rely on AI not just for efficiency but as a decision-support tool, guiding strategy and risk management. In entertainment, the impact is striking—about one in five new games on Steam now integrate generative AI for visuals, storytelling, or interactive features—highlighting the technology’s growing role in creative production.
Dataknobs is leveraging generative AI to build advanced data products such as stock market signals and equipment health indices, while also creating integrated knowledge solutions that combine knowledge generation, website development, and intelligent chatbot creation.
A CIO building an annual budget for generative AI should start by identifying high-impact, low-risk areas where AI can be applied within IT systems, such as automating service desk requests, accelerating code development, and improving system monitoring. By prioritizing these internal use cases, the CIO can create measurable cost savings and efficiency gains that justify further investment. The budget should allocate funds not only for technology adoption but also for workforce training, governance, and security, ensuring that generative AI is implemented responsibly and delivers sustainable value.
Google, Microsoft, and AWS are all rapidly expanding their generative AI ecosystems to capture enterprise and consumer demand.
Generative AI focuses on creating new content—such as text, images, or code—by learning patterns from data and producing original outputs. Predictive AI, on the other hand, analyzes historical data to forecast future outcomes, trends, or behaviors without generating new content.
A Large Language Model (LLM) is an advanced AI system trained on vast datasets to understand, process, and generate human-like language. Beyond basic conversation, LLMs are widely applied in code generation, CSS design automation, and a range of NLP tasks such as document analysis, summarization, and entity extraction. They also enable structured data analysis, helping organizations interpret complex datasets, uncover insights, and automate workflows. With these capabilities, LLMs power applications across industries—from intelligent chatbots and virtual assistants to advanced developer tools and enterprise knowledge systems.
Closed-source LLMs, like OpenAI’s GPT-5 or Anthropic’s Claude, are proprietary models where access is limited through APIs or subscriptions, offering high performance but little transparency or customization. Open-source LLMs, such as Meta’s Llama or Mistral, make their model weights publicly available, enabling developers to fine-tune, deploy, and innovate freely, often with strong community support.
| Comparison Dimension | Description |
|---|---|
| Model Size (# of Parameters) | Number of parameters (e.g., billions/trillions) affecting complexity, quality, and compute needs. |
| Accuracy & Benchmark Performance | Scores on MMLU, GSM8K, HumanEval, etc., indicating reasoning, coding, and knowledge strength. |
| Open vs. Closed Source | Open-weight models allow full customization and self-hosting; closed models offer managed APIs and guardrails. |
| Cost & Efficiency | Inference and training costs, token pricing, and hardware efficiency (GPU/TPU utilization). |
| Latency & Speed | Response time and throughput—critical for real-time chat, copilots, and streaming UX. |
| Multimodality | Support for text, images, audio, and video in input/output versus text-only models. |
| Context Window Length | Maximum tokens the model can consider at once—important for long documents and multi-turn sessions. |
| Fine-Tuning & Customization | Options for fine-tuning, adapters, or parameter-efficient methods to fit domain tasks. |
| Governance & Safety | Content filtering, policy guardrails, auditability, and compliance features. |
| Ecosystem & Tool Integration | SDKs, plugins, RAG/search tools, and cloud integrations (Azure, AWS, Google Cloud, Hugging Face). |
Guardrails are essentially guidelines and controls that steer the LLM's outputs in the desired direction.
Here are some ways to keep your LLM on track:
| Guardrail | Practices |
|---|---|
| Clear use-case & policy scope | Define allowed/blocked intents, refusal rules, and escalation paths. |
| Privacy & data minimization | Strip/obfuscate PII, control retention, encrypt data in transit and at rest. |
| Input validation & prompt hardening | Templatize prompts, sanitize user/tool inputs, and block jailbreak patterns. |
| Prompt-injection defenses | Filter untrusted content (RAG docs, web pages) and isolate it from system instructions. |
| Grounding to trusted sources | Use RAG/knowledge graphs; require citations and confidence signals to curb hallucinations. |
| Safety & content filters | Toxicity, hate, self-harm, IP/PII leakage, and malware classifiers on inputs and outputs. |
| Bias & fairness checks | Run bias evaluations, adjust datasets/prompts, and document mitigations. |
| Human-in-the-loop gates | Require review for high-risk actions (code deploys, financial trades, customer emails). |
| Tool/agent safety | Least-privilege keys, allow/deny lists, timeouts, cost/iteration budgets, sandboxed execution. |
| Access control & auditability | RBAC, per-tenant isolation, comprehensive logs, and immutable audit trails. |
| Rate limiting & abuse prevention | Quotas, anomaly detection, circuit breakers/kill switches. |
| Evaluation & red-teaming | Task-specific benchmarks, adversarial testing, and pre-prod safety bars. |
| Monitoring & drift detection | Track quality, safety incidents, latency/cost; alert on regressions and model drift. |
| Change management | Version prompts/models/tools, run A/B or shadow tests, maintain rollback plans. |
| Compliance & provenance | Map to SOC2/ISO/GDPR/CCPA; add watermarking or provenance tags where applicable. |
A foundation model is a big, pre-trained AI you can quickly adapt to many jobs—chat, search, code, document analysis—without starting from scratch. Examples include OpenAI GPT-4o, Anthropic Claude 3.5, Google Gemini 1.5 Pro, Meta Llama 3 (70B), and Mistral Large. From your perspective, you plug one into your app, point it at your knowledge (via RAG or a small fine-tune), set guardrails, and immediately start asking it to draft emails, summarize PDFs, write SQL, or generate UI/CSS—while you monitor cost, latency, and quality with simple knobs.
Retrieval-Augmented Generation. It's a technique that helps large language models. Before answering your question, the LLM uses RAG to search a vast external knowledge base for relevant information.
With this extra knowledge, the LLM can provide more accurate and informative answers. RAG combines the vast knowledge of LLMs with your data, enhancing AI's ability to provide contextually rich responses.
Vector DB store date in vector embeddings which are high dimension vectors that represent features or attributes of data. Traditional DB store dat in rows and columns. In traditional db each columen has signle field and each row is a record.
Traditional DBs are good for extact match vs vector dbs are great for similarity search.
Generative AI is based on "comprehend existing" data and determine trajectories data can take. It uses it for generation. Generative AI works by comprehending existing data, identifying patterns, and mapping the possible trajectories that data can take. It then leverages these insights to generate new content—such as text, images, or solutions—that aligns with the learned structures while exploring novel variations.
Traditional machine learning model has evaluaition metrics like accuracy. Generative AI creates new data. It evaluation is based on subjecive measures like diversity of data, realism, nobalty, creativity. It is hard to evaluate or benchmark geenrative models.
Use above dimensions to identify quardant of your use case(s). Low risk and applicability of generic data e.g content writing for sales,travel guide are easy to adopt.
Generative AI has potential, but there are many challenges and open questions. One should consider these before using geenrative AI in enterprise(s)
Generative AI is based on "comprehend existing" data and determine trajectories data can take. It uses it for creating new data. However the method of producing new variation of data makes it controllable.
There is significant amount of compute, energy usage. There is significant amount of carbon emission. One need to ensure it is for good cause and not for producing variation(s)
OpenAI function calling is a powerful feature that enables seamless integration of AI models with structured tasks, APIs, or external systems. By defining specific functions and providing their schemas, developers can guide the AI to trigger those functions only when appropriate, enhancing the model's capability to interact with external data and services. This approach allows for greater control and precision, enabling use cases such as querying databases, fetching live information, executing computations, or even managing workflows. Function calling ensures that responses remain contextually relevant and action-oriented, bridging the gap between conversational AI and real-world applications while maintaining flexibility and scalability in AI-driven solutions.
We recognize the immense potential of LLMs to revolutionize various aspects of our lives. However, we also acknowledge the critical need to ensure their development and deployment are guided by ethical principles and safeguard human values. Above are guiding principles and framework for AI. It is further extended for GenAI. Click to see detail slides for personalziation, automation and creative scenario to specific governance items
Establish AI Governance across enterprise. Have action plan to secure data, infrastructure and Model. See more details by clicking on slides
GPT 4o is latest model released by Open AI. It accept text and image as input and produce text output. It can reason across vision, text and audio
Use GPT 3.5 Turbo for ordinary task. It is cheapest. Use GPT 4o for complex task that need high quality
DALLE, TTS, Whisper are model for images, sound
GPT (Generative Pre-Trained Transformer) is family of large language model. developed by OPEN AI. GPT4 is latest generaral purpose LLM released by OpenAI. ChatGPT4 is chatbot focused LLM.
A Digital Human is an AI-powered virtual being that interacts with people naturally, combining advanced speech, vision, and emotional intelligence to simulate human-like conversations. These AI-driven personas can be used in customer service, education, healthcare, and entertainment, providing personalized and engaging interactions. With realistic facial expressions, voice modulation, and contextual awareness, digital humans bridge the gap between technology and human communication, making digital experiences more intuitive and lifelike. As AI and deep learning continue to evolve, digital humans are becoming an integral part of business and everyday life, enhancing engagement and efficiency in human-computer interactions.
Understand user intent better and improve search with embeddings. With Generative AI - do persona development and create personalized responsed. With GenAI do Proactive threat detection. Generative AI can also simulate cyber attack and help prepare model and advance technqies to handle such attacks.
GenAI is useful for Mortgage Industry
It can do document analysis in loan origination process and streamline it. It can help in customer service. Most important it can automate compliance in mortgage industry
GenAI can revolutionze mobile app development.
New apps willemerge for creativity. Better personalization will be provided in future apps.
Agent AI is a powerful, autonomous assistant designed to streamline tasks, provide real-time insights, and enhance decision-making across industries. Whether you're managing customer interactions, analyzing data, or automating workflows, Agent AI adapts to your needs using advanced AI models and contextual understanding. With seamless integrations, continuous learning, and personalized recommendations, Agent AI empowers businesses to operate more efficiently while delivering exceptional user experiences.
Dataknobs capablities - KREATE, KONTROLS and KNOBS.
KREATE focus on creatibvity and generation
KONTROLS provide guardrails, lineage, compliance, privacy and security.
KNOBS enable experimentation and diagnosis