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Browsing: Prompt
Three terms now compete for the same line in AI engineering job descriptions. Prompt engineering is the established one. Loop engineering entered the AI vocabulary in…
TL;DR variable rename passes Git, greenlights your mocked test suite, and gets approved in code review—right up until it crashes every real production call the moment…
was a very hot topic a few years ago. This is where you would tweak your prompt, even just a bit — for example, the format…
(Part A and Part B) we built the upgraded pipeline and watched it work: pointed at a research paper, a NIST standard, and a report with…
This week, OpenAI published details of GPT-Red, an internal-only automated red-teaming model. Its job is to attack OpenAI’s own models and find prompt injection vulnerabilities. OpenAI…
part of the generation brick of Enterprise Document Intelligence, a series that builds an enterprise RAG system from four bricks: document parsing, question parsing, retrieval, and…
Prompts are not static config files. Every instruction you add changes the behaviour of every query type the prompt already handles. Most teams catch prompt failures…
Getting prompts right is still the hardest part of shipping reliable LLM applications. Small wording changes can swing accuracy by 20 percent. What works on a…
k = RUN_KNOBS train_out = run_cli([“python”,”scripts/train.py”,”–config”,CFG,”–split_dir”,SPLIT, “–optimizer_model”,OPTIMIZER_MODEL,”–target_model”,TARGET_MODEL,”–out_root”,RUN, *COMMON, “train.train_size=0″, f”train.num_epochs={k[‘num_epochs’]}”, f”train.batch_size={k[‘batch_size’]}”, f”gradient.minibatch_size={k[‘minibatch’]}”, f”gradient.merge_batch_size={k[‘merge_batch’]}”, f”gradient.analyst_workers={k[‘workers’]}”, f”optimizer.learning_rate={k[‘lr’]}”, f”optimizer.lr_scheduler={k[‘lr_sched’]}”, “optimizer.use_slow_update=true”, “optimizer.use_meta_skill=true”, f”env.workers={k[‘workers’]}”, f”env.limit={k[‘limit’]}”], “TRAIN (rollout->reflect->aggregate->select->update->gate; slow-update + meta-skill)”) import…
def make_problems(n, seed=0): rng = random.Random(seed) out = [] for _ in range(n): t = rng.choice([“discount”, “travel”, “wallet”, “chain”]) if t == “discount”: unit = rng.choice([40,…
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