Generative AI Architectures and Prompt Design

Vision-language models link images and text for understanding and generation, while unified architectures combine multiple modalities into a single model, enabling efficient and versatile multimodal AI systems.

Audio and video generation models create realistic sound and visual content using advanced generative techniques. Systems like AudioLDM and Phenaki, along with Sora-inspired principles, enable high-quality, temporally coherent multimedia generation from text and other conditions.

Agentic multimodal architectures use multiple collaborating agents and external tools to perform complex reasoning and decision-making across different data modalities, improving adaptability and problem-solving capability.

RAG enhances model responses by grounding generation in retrieved external knowledge, while LoRA and QLoRA enable efficient fine-tuning by updating only a small number of parameters, reducing compute and memory costs.

RLHF aligns models with human preferences using reward-based reinforcement learning, while DPO offers a simpler and more efficient alternative by directly optimizing preference data without a reward model.

Test-time training improves model adaptability during inference, while adaptive compute techniques like speculative decoding and MAD dynamically reduce computation, enabling faster and more efficient model deployment without sacrificing quality.

Zero-shot and few-shot prompting enable models to perform tasks with little or no training data, while chain-of-thought prompting improves reasoning by guiding the model through intermediate steps.

Role-playing improves contextual relevance, structured formats ensure reliable and machine-readable outputs, and temperature control balances creativity and consistency in model responses.

Prompt compression reduces token usage without losing critical context, while iterative refinement improves output quality through repeated feedback and adjustments.

Tree-of-Thoughts, graph prompting, and self-consistency are techniques that enhance reasoning in language models by exploring multiple solution paths, structuring relationships between ideas, and selecting the most consistent answer from several reasoning attempts, leading to more accurate and reliable outputs.

Automatic prompt optimization methods like APE and OPRO automate the discovery of effective prompts by iteratively testing and refining them, while meta-prompting leverages higher-level instructions that guide models to create and improve prompts themselves, resulting in more efficient, scalable, and consistent prompt design.

Automatic prompt optimization methods like APE and OPRO automate the discovery of effective prompts by iteratively testing and refining them, while meta-prompting leverages higher-level instructions that guide models to create and improve prompts themselves, resulting in more efficient, scalable, and consistent prompt design.

LLM-as-Judge and G-Eval provide scalable, automated ways to evaluate language model outputs using model-based scoring and structured criteria, while hallucination detection focuses on identifying factual inconsistencies or unsupported claims, together ensuring more accurate, reliable, and trustworthy AI systems.

Constitutional AI uses predefined principles to guide and self-correct model behavior, while red-teaming stress-tests models through adversarial prompts to uncover weaknesses and risks. Bias mitigation complements these approaches by reducing unfair patterns in training data and model outputs.

Effective production deployment combines robust API integration, proper rate limiting to ensure scalability and fairness, and comprehensive monitoring to track performance, usage, and failures, ensuring reliable, secure, and maintainable AI systems.