More Software Engineering Radio episodes
Published 8 Apr 2026
Duration: 48:02
Ambiguity in language and speech, arising from context, phrasing, and incomplete information, poses challenges for AI systems due to their limited context processing, while humans resolve it through contextual cues, tone, and prior knowledge, with strategies focusing on contextual prompts, audio training, data augmentation, and balancing AI efficiency with human-like adaptability in multilingual and ethical contexts.
Sahaj Garg, co-founder and CTO of Wispr, a voice-to-text AI that turns speech into polished writing, talks with host Amey Ambade about designing syste...
The podcast episode examines the concept of ambiguity in human input, distinguishing it from noise and errors as an inherent property of unclear or multifaceted information. It highlights how humans resolve ambiguity using context, tonal cues, and prior knowledge, while machine learning models face challenges due to limited context windows, which hinder their ability to process and interpret ambiguous inputs effectively. The discussion extends to speech-to-text conversion, where unstructured spoken languagemarked by slang, filler words, and varying formalitiesrequires context-aware processing to adapt to different communication styles and user intent. Key challenges include handling background noise, accents, jargon, and the need for models to leverage contextual information to improve accuracy, especially in voice-first systems like Whisper.
The episode further explores types of ambiguity, such as polysemous words, sentence structure confusion, and stylistic variations in language depending on the audience (e.g., texting vs. professional communication). It addresses the limitations of traditional audio models and the potential of large language models (LLMs) in integrating context, history, and external prompts to enhance speech recognition. Strategies for improving model performance include contextual training with vocal metadata, data augmentation, and refining outputs through instruction tuning aligned with user preferences. The discussion also touches on balancing personalization with consistency, the role of user feedback in refining AI systems, and the importance of context compression and inference optimization in managing ambiguity and ensuring efficient, accurate AI interactions.
20 May 2026 Rob Moffat on Risk-First Software Development
Recommended: Risk identification and management is a forgotten art
Software development prioritizes risk management through frameworks like test-driven development and agile, addressing hidden risks, AI deployment challenges, open-source dependencies, and organizational prioritization to balance innovation with safeguards.
13 May 2026 SE Radio 720: Martin Dilger on Understanding Eventsourcing
Recommended: Useful Architectural Pattern.
Event sourcing is a system design approach that records changes as sequential events to ensure historical traceability, uses event modeling for aligning systems with human workflows, contrasts with CRUD architectures, and emphasizes slice-based design, event streams, and practical applications like legacy modernization and workflow simplification.
6 May 2026 Birol Yildiz on Building an Agentic AI SRE
AI agents in SRE leverage autonomous decision-making, agentic search, and lightweight architectures to replace static runbooks, balancing autonomy with reliability challenges, context management, and human oversight in dynamic environments.
29 Apr 2026 Will Sentance on JS Modernization
JavaScript's evolution from a 1995 scripting language to a performance-optimized modern tool balances innovation with backward compatibility through TC39's incremental updates, browser advancements, community-driven libraries, key features like async/await and symbols, engine optimizations, and a design philosophy prioritizing flexibility and user-driven standardization for large-scale frameworks.
23 Apr 2026 Eric Tschetter on Decoupling Observability
Recommended: Telemetry is important, avoiding vendor lockin is even more important.
Observability in microservices emphasizes decoupled architectures over traditional frameworks to address vendor lock-in, data interoperability, and scalability challenges, while balancing unstructured telemetry management, query language standardization, and cross-team collaboration.