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.
| Dimension | Stay with Base Foundation Model | Extend Foundation Model (Fine-tune / LoRA / RAG) |
|---|---|---|
| Accuracy & Fit | Good for general use; weaker on niche tasks. | Stronger performance in specialized domains or tasks. |
| Differentiation | Limited uniqueness; competitors can replicate. | Proprietary advantage and domain-specific strength. |
| Speed to Market | Fast to deploy. | Slower — requires data curation and training. |
| Data Needs | Minimal/no custom dataset needed. | Requires curated, representative domain data. |
| Maintainability | Easy to upgrade as base model improves. | Harder to maintain; fine-tuned models can get stale. |
| Compliance / Risk | Higher risk of inaccuracies in sensitive domains. | Lower risk if grounded in curated, audited data. |
| Cost | Lower cost (no training, cheaper inference). | Higher cost (training + possible inference overhead). |
| Scalability | Efficient, lightweight. | May increase latency/compute needs (depends on method). |
👉 In short:
Prompt engineering is the practice of designing and refining the inputs (prompts) given to a foundation model so that it produces reliable, accurate, and useful outputs without retraining the model. It involves carefully choosing wording, structure, and context—such as providing role instructions (“You are an expert lawyer”), formatting examples, or step-by-step reasoning cues—to guide the model’s behavior. Effective prompt engineering can significantly improve performance on specific tasks, reduce hallucinations, and tailor responses to user needs. It is often the fastest, lowest-cost way to adapt a foundation model before considering heavier approaches like retrieval augmentation or fine-tuning.
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.
RAG is extremly ueful when you need to answer question on specific domain, latest and up to date information or use internal knowledge. Use RAG is
Domain-specific knowledge: If your assistant needs to be an expert in a specific domain, RAG can be used to integrate relevant databases or knowledge repositories to enhance its understanding.
Accuracy is crucial: If your LLM assistant needs to provide highly accurate information, especially on factual topics like science, history, or specific procedures, RAG can ensure responses are grounded in real-world knowledge.
Combating hallucinations: LLMs can sometimes make up information, called hallucination. RAG combats this by providing verifiable evidence to support the response.
Building trust: By allowing users to see where the information comes from (think footnotes!), RAG fosters trust and transparency in the assistant's responses.
The key advantage of RAG is flexibility. Unlike fine-tuning, where knowledge is baked into the model weights, retrieval can be updated dynamically—simply by adding or changing documents in the external source. This makes it ideal for domains with fast-changing information (like regulations, product catalogs, or research) and for scenarios where data privacy requires strict control over what the model can access.
Accuracy : Lead to user Trust and Effectiveness: Higher accuracy in understanding and responding to user queries builds trust and reliability.
Accurate AI assistants can effectively handle complex tasks, providing precise information and solutions.
Performance : Better Speed and Scale lear to quick response. High-performance AI assistants provide quick responses, improving user experience and efficiency.
Efficient performance ensures that the AI can handle numerous requests simultaneously, essential for scaling operations.
Cost : Lead to adoption and ROI generation. Lower costs can make AI assistants accessible to a broader range of users and businesses.
Affordable solutions encourage wider adoption, enabling more industries to benefit from AI.
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.
A vector database is a specialized system designed to store, index, and search high-dimensional vectors—numerical representations of data such as text, images, audio, or video. These vectors are generated by machine learning models (like embeddings from LLMs) and capture the semantic meaning of the data. Vector databases use algorithms like approximate nearest neighbor (ANN) search to quickly find the most similar vectors, enabling tasks such as semantic search, recommendation systems, document retrieval, and anomaly detection. Popular vector databases include Pinecone, Weaviate, Milvus, and FAISS.
You would use a vector database over a traditional database when the goal is to retrieve information based on meaning or similarity rather than exact matches. Traditional databases excel at structured queries (e.g., looking up rows by exact keys, ranges, or filters), but they struggle with unstructured or fuzzy data like “find documents similar to this one” or “recommend songs that feel like this track.” Vector databases shine in these scenarios because they can handle unstructured data efficiently, scale to millions or billions of items, and deliver low-latency similarity search results that power modern AI-driven applications.
Traditional DBs are good for extact match vs vector dbs are great for similarity search.
Vector Dbs have great use in many use cases like:
Text Search, Question Answer Bot, Vision search, Find similar design, Efficient analysis of audio, Find visually similar images e.g. blue water
OpenAI provides robust fine-tuning capabilities that allow developers to adapt base models to their specific needs. Fine-tuning lets organizations train models on their own datasets so the outputs align more closely with their domain, brand voice, or task requirements. Instead of building a model from scratch, developers can start with a powerful foundation model and refine it with supervised training examples. OpenAI supports structured fine-tuning workflows, including preparing datasets, training custom variants, and evaluating model performance. This enables applications like domain-specific chatbots, personalized assistants, and tailored classification or generation tasks. Additionally, OpenAI provides monitoring, evaluation, and versioning tools, so teams can iterate safely and ensure that their fine-tuned models meet performance and compliance standards.
However, before attempting fine-tuning, it’s often more efficient to try prompt engineering or few-shot learning, as these approaches can achieve strong results with minimal effort. They are faster, cheaper, and easier to iterate on, helping determine whether fine-tuning is truly necessary.
The process of fine-tuning an OpenAI model generally involves several key steps. First, you need to prepare and clean your dataset, ensuring it is in the correct JSONL format with high-quality examples that reflect the desired input-output behavior. Next, you upload the dataset to OpenAI’s platform and run validation checks to confirm the data is properly structured. After that, you train the model using OpenAI’s fine-tuning API, where the base model is adapted on your custom data to learn specific patterns or domain knowledge. Once training is complete, you’ll receive a custom fine-tuned model ID, which can be used in place of the base model for API calls. The final step is to evaluate and iterate: test the model’s performance against real-world prompts, refine the dataset if needed, and re-train to continually improve results.
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.
Generative AI (GenAI) models come in various architectures, each designed to handle specific types of tasks. The most widely used are Large Language Models (LLMs) based on the transformer architecture, which uses self-attention mechanisms to understand and generate human-like text. Transformers power models such as GPT, PaLM, and LLaMA, enabling them to capture long-range dependencies and context efficiently. Beyond text, multimodal architectures like CLIP and Flamingo combine language and vision, allowing models to interpret images alongside text. Diffusion models, such as Stable Diffusion and DALL·E, are another branch of GenAI that excel at image generation, gradually transforming noise into coherent visuals guided by textual prompts. Similarly, models like Whisper specialize in speech-to-text, leveraging sequence-to-sequence learning tuned for audio inputs.
LLM architectures also vary by design goals and efficiency strategies. Decoder-only models (e.g., GPT-4, LLaMA) excel at generative tasks like writing, coding, and reasoning, while encoder-decoder models (e.g., T5, FLAN-T5) are particularly strong in translation, summarization, and instruction following. Some models use Mixture of Experts (MoE) architectures, like Google’s Switch Transformer, which activate only subsets of parameters during inference to balance scale with efficiency. Others explore retrieval-augmented generation (RAG), integrating external knowledge sources such as vector databases to extend a model’s effective memory. These variations reflect the broader trend of tailoring architectures to achieve better performance, efficiency, and adaptability across diverse generative AI applications.
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