RAG vs. Finetuning: Enhancing LLMs with new knowledge
Some language models have no knowledge beyond their 2022-dated training data. Methods like retrieval-augmented generation (RAG) and finetuning aim to fix that.
From DAN to Universal Prompts: LLM JailbreakingDeepgram
AI Phone Agent for Patient Intake: What Happens Before the AppointmentDeepgram
Why TTS Latency Grows With Response LengthDeepgram
Nova-3 Adds 12 New Speech-to-Text LanguagesDeepgram
Voice Tech is Growing. How can that Happen Equitably?Deepgram
Deepgram and AWS Amazon Connect Integration to Unlock Voice Data at ScaleDeepgram
How Ramp automated receipt processing with fine-tuned LLMsModal
How AI Assistants Can Decode GitHub Repos for UI WritersDocker
A prompt guide to get the most out of HeyGen's new Video AgentHeygen
Enhancing Pulumi Copilot: Introducing System Prompts for Your OrganizationPulumi
We're Pushing User Experience Forward With Real-time Global Config PropagationBunny
How to structure documentation for both AI and human readersMintlify
SuperGLUE: Understanding a Sticky Benchmark for LLMsDeepgram
ChatGPT: Putting the “AI” in “Plagiarism” worldwide?Deepgram
Policy Comes to Team and Enterprise, with New Out-of-the-box Policies and Management ExperiencePulumi
What is RetoolGPT? How we built an internal AI assistantRetool
Just Enough SQL to Safely Use AI for Data AnalysisMotherduck
Cerebras 2024 Predictions for Generative AI, LLMs, and HPCCerebras
Healthcare Digital Marketing StrategiesHeygen
Free llms.txt generator: create AI-optimized documentation filesMintlify
