organize your code in the same way you would if it were synchronous. This is especially important for error handling, since try/except blocks work as you would expect in coroutines while this is difficult to parallel_fetch_many(urls): responses = await multi ([http_client.fetch(url) for url in urls]) # responses is a list of HTTPResponses in the same order async def parallel_fetch_dict(urls): responses = await multi({url: multi({url: http_client.fetch(url) for url in urls}) # responses is a dict {url: HTTPResponse} In decorated coroutines, it is possible to yield the list or dict directly: @gen.coroutine def parallel_fetch_decorated(url1

Application object Subclassing RequestHandler Handling request input Overriding RequestHandler methods Error Handling Redirection Asynchronous handlers Templates and UI Configuring templates Template syntax organize your code in the same way you would if it were synchronous. This is especially important for error handling, since try/except blocks work as you would expect in coroutines while this is difficult to s): responses = await multi ([http_client.fetch(url) for url in urls]) # responses is a list of HTTPResponses in the same order async def parallel_fetch_dict(urls): responses = await multi({url:

agreed-upon-metrics for accuracy, relevance, and coherence. 03 Human trainers Comparing AI results to responses from expert advisors, grading for accuracy and relevance. These evals—and others—gave Morgan fine-tuning OpenAI models, the Lowe’s team was able to improve product tagging accuracy 
 by 20%—with error detection improving by 60%. Excitement in the team was palpable when we saw results from fine-tuning were getting bogged down, spending time accessing systems, trying to understand context, craft responses, and take the right actions for customers. So we built an internal automation platform. It works

failure, tested for failure, and evolved beyond it b. ESTABLISH A JUST, LEARNING CULTURE i. Unjust responses to incidents 1. Impede safety 2. Promote fear over mindfulness 3. Create bureaucracy rather Bad Apple Theory – you cannot eliminate error by eliminating the people who caused the error. 1. “Human error is not our cause of troubles; instead, human error is a consequence of the design of the

Examples of ineffective quality controls include: a. Requiring another team to complete tedious, error-prone, and manual tasks that could be easily automated and run as needed by the team who needs the batches of work to teams and special committees for approval and processing and then waiting for responses 3. Instead a. Use peer reviews of our proposed changes to gain whatever assurance is needed that

Stanford HAI (4/25) AI Development Trending = Unprecedented42 AI Performance = In Q1:25… 73% of Responses & Rising Mistaken as Human by Testers Note: The Turing test, introduced in 1950, measures a machine’s ‘Large Language Models Pass the Turing Test’ (3/25) via UC San Diego % of Testers Who Mistake AI Responses as Human-Generated – 3/25, per Cameron Jones / Benjamin Bergen Date Released 5/24 1/25 2/25 indistinguishable from that of a human. In the test, if a human evaluator cannot reliably tell whether responses are coming from a human or a machine during a conversation, the machine is said to have passed

Extracting useful information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411 33.3 Error handling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412 33.4 Exception give you an error: julia> pi π = 3.1415926535897... julia> pi = 3 ERROR: cannot assign a value to imported variable Base.pi from module Main julia> sqrt(100) 10.0 julia> sqrt = 4 ERROR: cannot assign julia> x, ___ = size([2 2; 1 1]) (2, 2) julia> y = ___ ERROR: syntax: all-underscore identifier used as rvalue julia> println(___) ERROR: syntax: all-underscore identifier used as rvalue The only

Extracting useful information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411 33.3 Error handling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412 33.4 Exception give you an error: julia> pi π = 3.1415926535897... julia> pi = 3 ERROR: cannot assign a value to imported variable Base.pi from module Main julia> sqrt(100) 10.0 julia> sqrt = 4 ERROR: cannot assign julia> x, ___ = size([2 2; 1 1]) (2, 2) julia> y = ___ ERROR: syntax: all-underscore identifier used as rvalue julia> println(___) ERROR: syntax: all-underscore identifier used as rvalue The only

Extracting useful information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438 34.3 Error handling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439 34.4 Exception give you an error: julia> pi π = 3.1415926535897... julia> pi = 3 ERROR: cannot assign a value to imported variable Base.pi from module Main julia> sqrt(100) 10.0 julia> sqrt = 4 ERROR: cannot assign 2; 1 1]) (2, 2) julia> y = ___ ERROR: syntax: all-underscore identifiers are write-only and their values cannot be used in expressions �→ julia> println(___) ERROR: syntax: all-underscore identifiers

Extracting useful information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438 34.3 Error handling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439 34.4 Exception give you an error: julia> pi π = 3.1415926535897... julia> pi = 3 ERROR: cannot assign a value to imported variable Base.pi from module Main julia> sqrt(100) 10.0 julia> sqrt = 4 ERROR: cannot assign 2; 1 1]) (2, 2) julia> y = ___ ERROR: syntax: all-underscore identifiers are write-only and their values cannot be used in expressions �→ julia> println(___) ERROR: syntax: all-underscore identifiers