#Generative AI

Articles about Generative AI — exploring patterns, best practices, and real-world implementations in production systems.

3 posts tagged with generative ai. ← All posts

#A/B Testing (3)#A2A (14)#ADK (8)#AG-UI (6)#AI (9)#AI Agents (335)#AI Architecture (22)#AI Cost (10)#AI Cost Optimization (8)#AI Engineering (282)#AI Evaluation (9)#AI Financial Engineering (14)#AI Gateway (8)#AI Governance (40)#AI Red Teaming (9)#AI Research (9)#AI Safety (12)#AI Security (29)#AI in Production (12)#AML (3)#API Design (10)#API Security (8)#APIs (59)#AWS (17)#Accountability (8)#Accounting (9)#Agent Skills (11)#Agentic AI (25)#Agentic Commerce (12)#Agentic RAG (8)#Agents (4)#Amazon Bedrock (8)#Analytics (3)#Architecture (42)#Attention (3)#Audit (4)#Authentication (11)#Authorization (3)#Automation (11)#Azure (11)#Azure AI Foundry (9)#Backend Engineering (310)#Benchmarks (3)#Best Practices (4)#BigQuery (6)#Business Finance (8)#Business Strategy (55)#C (8)#CI/CD (16)#Caching (11)#Capital Markets (14)#Card Payments (12)#Cards (13)#Career (25)#Causal Inference (8)#Cellular Automata (8)#Checkpointing (4)#Claude Code (8)#Cloud (5)#Cloud Architecture (3)#Cloud Native (10)#Code Review (9)#Coding Agents (8)#Collaboration (5)#Communication (9)#Complexity (8)#Compliance (54)#Computer Networking (9)#Computer Science (40)#Computer Vision (15)#Computer-Use Agents (8)#Concurrency (40)#Consulting (5)#Containers (10)#Context Engineering (14)#Conversational AI (8)#Cost Optimisation (5)#Cost Optimization (3)#Credit (16)#Credit Risk (16)#CrewAI (8)#Crypto (12)#Cryptocurrency (12)#Cryptography (9)#Custody (9)#DSPy (8)#Data (14)#Data Engineering (24)#Data Science (8)#Data Structures (10)#Databases (41)#Deep Learning (20)#Deployment (5)#Design (3)#Design Patterns (10)#DevOps (24)#DevSecOps (22)#Developer Experience (5)#Developer Tools (5)#Differential Privacy (3)#Distributed Systems (100)#Documentation (3)#Edge AI (8)#Embeddings (21)#Emotional Intelligence (8)#Energy (8)#Engineering (14)#Engineering Culture (3)#Engineering Judgment (7)#Engineering Practices (16)#Error Handling (4)#Evaluation (68)#Event-Driven Architecture (8)#Explainable AI (8)#FREE-AI (8)#FX (5)#Feature Engineering (3)#Feedback (4)#FinOps (23)#FinTech (6)#Financial AI (14)#Financial Engineering (8)#Financial Systems (129)#Fine-Tuning (12)#Fintech (131)#Flutter (8)#Forecasting (10)#Foreign Exchange (5)#Forward Deployed Engineer (22)#Forward Deployed Engineering (8)#Fraud (10)#Function Tools (5)#Functional Programming (3)#Fundamentals (23)#Fundraising (8)#GCP (5)#GPU (8)#GUI Automation (8)#Gemma (4)#Generative AI (3)#Git (8)#Go (220)#Go-to-Market (13)#Google ADK (36)#Governance (61)#Granite (6)#Graph Neural Networks (8)#GraphQL (3)#Growth (3)#Guardrails (34)#HIPAA (3)#HTTP (3)#Harness Engineering (8)#Hiring (8)#Hugging Face (8)#Human-in-the-Loop (9)#IBM watsonx (8)#Identity (11)#Inference (8)#Information Retrieval (8)#Integration (3)#Intellectual Property (8)#Interfaces (3)#Interpretability (8)#JavaScript (8)#KYC (11)#KYC and AML (12)#Kafka (11)#Knowledge Graphs (10)#Kubernetes (17)#LLM (5)#LLM Inference (8)#LLM Infrastructure (8)#LLM-as-Judge (3)#LLMOps (8)#LLMs (192)#LangChain (8)#LangGraph (11)#Leadership (29)#Ledger (12)#Legal (8)#Lending (14)#Linux (9)#LlamaIndex (8)#Load Balancing (3)#MCP (22)#MLOps (36)#Machine Learning (109)#Marketing (16)#Markets (4)#Mathematical Finance (8)#Memory (15)#Memory Management (5)#Metrics (7)#Microservices (3)#Microsoft Agent Framework (150)#Middleware (6)#Migration (9)#Mixture of Experts (5)#Model Risk (3)#Monitoring (5)#Multi-Agent (10)#Multi-Agent AI (14)#Multi-Agent Systems (73)#Multimodal (3)#Multimodal AI (11)#NIM (5)#NVIDIA (8)#Networking (3)#OAuth (3)#OWASP (7)#Observability (50)#On-Device AI (8)#Ontology (8)#Open Source (7)#OpenTelemetry (5)#Operating Systems (9)#Operations (13)#Opinion (6)#Orchestration (10)#Organizational Design (8)#Payment Rails (16)#Payments (54)#People (8)#Performance (56)#Personalization (9)#Platform Engineering (9)#Policy as Code (3)#PreSales (8)#Privacy (13)#Privacy Engineering (3)#Process (4)#Product (30)#Product Management (8)#Production (22)#Programming (10)#Programming Languages (48)#Prompt Engineering (80)#Prompt Injection (15)#Protocol Buffers (3)#Protocols (9)#Providers (4)#Pydantic AI (8)#Python (142)#Quality (3)#Quantitative Finance (22)#RAG (70)#RBI (3)#REST (5)#Rails (16)#Real-Time (8)#Reasoning Models (8)#Recommender Systems (8)#Reconciliation (3)#RegTech (8)#Regulation (10)#Reinforcement Learning (9)#Reliability (56)#Resilience (4)#Responsible AI (5)#Retrieval (3)#Risk (14)#Risk Management (11)#Rust (32)#SLSA (3)#SRE (23)#Sales (9)#Scalability (6)#Search (10)#Security (102)#Security Engineering (8)#Self-Evolving Agents (16)#Semantic Web (8)#Sessions (3)#Settlement (9)#Soft Skills (8)#Software (3)#Software Architecture (36)#Software Delivery (9)#Software Engineering (152)#Spanner (4)#Speech (8)#Startups (30)#Statistics (8)#Strands (8)#Strategy (6)#Stream Processing (8)#Streaming (36)#Structured Output (4)#Supply Chain Security (9)#Sustainability (8)#System Design (32)#Systems Programming (56)#Testing (66)#Threat Modeling (3)#Time Series (9)#Tool Use (23)#Tooling (5)#Tools (4)#Trading (8)#Treasury (6)#Trust (3)#Type System (3)#Type Systems (11)#TypeScript (8)#Validation (3)#Valuation (3)#Vector Databases (22)#Vector Search (11)#Venture Capital (8)#Version Control (9)#Voice AI (9)#Web Development (6)#Workflow (4)#Workflows (14)#eBPF (8)#gRPC (13)#smolagents (8)
Pratik Dhanave · ·9 min read

Building with Multimodal AI

The gap between "multimodal AI exists" and "I built something useful with it" is smaller than it's ever been — you can call a vision-language model, a speech recognizer, or an image generator with an API and a few lines of code. But building well with multimodal AI means knowing what these models can and can't reliably do, how to combine them, and where the pitfalls are. This closing post is the practical one: how to actually build multimodal applications, and where the whole field is heading — toward general, any-to-any models.

The gap between 'multimodal AI exists' and 'I built something useful with it' is smaller than ever — you can call a vision-language model, a speech recognizer, or an image generator with a few lines of code. But building well means knowing what these models can and can't reliably do, how to combine them, and where the pitfalls are. Plus the any-to-any future.

#Multimodal AI#AI Engineering#Machine Learning#Generative AI
Pratik Dhanave · ·8 min read

Video and Beyond

Video is where multimodal AI gets genuinely hard. It's not just images — it's images over time, plus audio, at a scale that dwarfs a single picture. The temporal dimension adds motion, causality, and continuity that a still frame can't capture, and the sheer data volume strains everything. Video is also the frontier where the most impressive recent generation results have appeared, and where multimodal AI is actively pushing forward. Understanding video — and the other modalities beyond the core ones — shows where the field is heading.

Video is where multimodal AI gets genuinely hard. It's not just images — it's images over time, plus audio, at a scale that dwarfs a single picture. The temporal dimension adds motion, causality, and continuity a still frame can't capture, and it's the frontier where the most impressive recent generation results have appeared.

#Multimodal AI#Generative AI#Machine Learning#AI Engineering
Pratik Dhanave · ·8 min read

Generating Images

Typing a sentence and watching a detailed, coherent, never-before-seen image appear is one of the most striking capabilities in modern AI — and the technique behind it is beautifully counterintuitive. Rather than paint an image stroke by stroke, these models start with pure noise and gradually remove it, step by step, sculpting a picture out of static, guided by your text. Understanding diffusion — and how text steers it — demystifies text-to-image generation and reveals one of the most important generative techniques in AI.

Typing a sentence and watching a detailed, never-before-seen image appear is one of the most striking capabilities in modern AI — and the technique behind it is beautifully counterintuitive. These models start with pure noise and gradually remove it, sculpting a picture out of static, guided by your text. That's diffusion.

#Multimodal AI#Generative AI#Computer Vision#AI Engineering#Machine Learning

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.