LLM to predict the right sequence of tokens. Prompt engineering is the process of designing high-quality prompts that guide LLMs to produce accurate outputs. This process involves tinkering to find the need for few-shot prompting depends on a few factors, including the complexity of the task, the quality of the examples, and the capabilities of the generative AI (gen AI) model you are using. As a general examples that are relevant to the task you want to perform. The examples should be diverse, of high quality, and well written. One small mistake can confuse the model and will result in undesired output.

agents 
 (see Orchestration). 10 A practical guide to building agents Configuring instructions High-quality instructions are essential for any LLM-powered app, but especially critical for agents. Clear instructions financial impact. Use these risk ratings to trigger automated actions, such as pausing for guardrail checks before executing high-risk functions or escalating to a human if needed. 26 A practical guide to Output validation Ensures responses align with brand values via prompt engineering and content checks, preventing outputs that 
 could harm your brand’s integrity. Building guardrails Set up guardrails

3%, and boosts the maximum generation throughput to 5.76 times. We pretrain DeepSeek-V2 on a high-quality and multi-source corpus consisting of 8.1T tokens, and further perform Supervised Fine-Tuning (SFT) training costs, and efficient inference throughput (Figure 1(b)), simultaneously. We construct a high-quality and multi-source pre-training corpus consisting of 8.1T tokens. Compared with the corpus used in 2024), this corpus features an extended amount of data, especially Chinese data, and higher data quality. We first pretrain DeepSeek-V2 on the full pre-training corpus. Then, we collect 1.5M conversational

measurable improvements on three fronts: 01 Workforce performance Helping people deliver higher-quality outputs in shorter 
 time frames. 02 Automating routine operations Freeing people from repetitive examples. 5 AI in the EnterpriseLesson 1 Start with evals How Morgan Stanley iterated to ensure quality and safety As a global leader in financial services, Morgan Stanley is a relationship business. clients.
 They started with three model evals: 01 Language translation Measuring the accuracy and quality of translations produced 
 by a model. 02 Summarization Evaluating how a model condenses information

Employed USA Adults AI User + Usage + CapEx Growth = Unprecedented 0% 25% 50% 75% 100% Improving the Quality of Their Work Allowing Them to Do Things More Quickly Extremely / Very Somewhat Not Too / Not general-purpose models may be accelerating commoditization and driving diminishing returns, as output quality converges across players and differentiation becomes harder to sustain. At the same time, the cost general-purpose models may be accelerating commoditization and driving diminishing returns, as output quality converges across players and differentiation becomes harder to sustain. At the same time, the

contributors at UW, AWS, and OctoML. e Initial implementation is quickly moving towards production quality. o _VM compiler VM runtime VM serialization Dynamic Shape Support Dynamic Shape Allocation o