PromptFlow

PromptFlow AI

PromptFlow AI

PromptFlow AI

PromptFlow AI

PromptFlow AI

PromptFlow AI

PromptFlow AI

PromptFlow AI

PromptFlow AI

PromptFlow AI

Cut Claude usage by ~85% in a job search pipeline (16k → 900 tokens/app) — here’s what worked

Like many here, I kept running into Claude usage limits when building anything non-trivial. I was working with a job search automation pipeline (based on the Career-Ops project), and the naive flow was burning ~16k tokens per application — completely unsustainable. So I spent some time reworking it with a focus on token efficiency as a first-class concern, not an afterthought. 🚀 Results ~85% reduction in token usage ~900 tokens per application Most repeated context calls eliminated Much more stable under usage limits ⚡ What actually helped (practical takeaways) 1. Prompt caching (biggest win) Cached system + profile context (cache_control: ephemeral) Break-even after 2 calls, strong gains after that ~40% reduction on repeated operations 👉 If you're re-sending the same context every time, you're wasting tokens. 2. Model routing instead of defaulting to Sonnet/Opus Lightweight tasks → Haiku Medium reasoning → Sonnet Heavy tasks only → Opus 👉 Most steps don’t need expensive models. 3. Precompute anything reusable Built an answer bank (25 standard responses) in one call Reused across applications 👉 Eliminated ~94% of LLM calls during form filling. 4. Avoid duplicate work TF-IDF semantic dedup (threshold 0.82) Filters duplicate job listings before evaluation 👉 Prevents burning tokens on the same content repeatedly. 5. Reduce “over-intelligence” Added a lightweight classifier step before heavy reasoning Only escalate to deeper models when needed 👉 Not everything needs full LLM reasoning. 🧠 Key insight Most Claude workflows hit limits not because they’re complex — but because they recompute everything every time. 🧩 Curious about others’ setups How are you handling repeated context? Anyone using caching aggressively in multi-step pipelines? Any good patterns for balancing Haiku vs Sonnet vs Opus? https://github.com/maddykws/jubilant-waddle Inspired by Santiago Fernández’s Career-Ops — this is a fork focused on efficiency + scaling under usage limits. submitted by /u/distanceidiot [link] [comments]

Is there something I can do about my prompts? [Long read, I’m sorry]

Hello everyone, this will be a bit of a long read, i have a lot of context to provide so i can paint the full picture of what I’m asking, but i’ll be as concise as possible. i want to start this off by saying that I’m not an AI coder or engineer, or technician, whatever you call yourselves, point is I’m don’t use AI for work or coding or pretty much anything I’ve seen in the couple of subreddits I’ve been scrolling through so far today. Idk anything about LLMs or any of the other technical terms and jargon that i seen get thrown around a lot, but i feel like i could get insight from asking you all about this. So i use DeepSeek primarily, and i use all the other apps (ChatGPT, Gemini, Grok, CoPilot, Claude, Perplexity) for prompt enhancement, and just to see what other results i could get for my prompts. Okay so pretty much the rest here is the extensive context part until i get to my question. So i have this Marvel OC superhero i created. It’s all just 3 documents (i have all 3 saved as both a .pdf and a .txt file). A Profile Doc (about 56 KB-gives names, powers, weaknesses, teams and more), A Comics Doc (about 130 KB-details his 21 comics that I’ve written for him with info like their plots as well as main cover and variant cover concepts. 18 issue series, and 3 separate “one-shot” comics), and a Timeline Document (about 20 KB-Timline starting from the time his powers awakens, establishes the release year of his comics and what other comic runs he’s in [like Avengers, X-Men, other character solo series he appears in], and it maps out information like when his powers develop, when he meets this person, join this team, etc.). Everything in all 3 docs are perfect laid out. Literally everything is organized and numbered or bulleted in some way, so it’s all easy to read. It’s not like these are big run on sentences just slapped together. So i use these 3 documents for 2 prompts. Well, i say 2 but…let me explain. There are 2, but they’re more like, the foundation to a series of prompts. So the first prompt, the whole reason i even made this hero in the first place mind you, is that i upload the 3 docs, and i ask “How would the events of Avengers Vol. 5 #1-3 or Uncanny X-Men #450 play out with this person in the story?” For a little further clarity, the timeline lists issues, some individually and some grouped together, so I’m not literally asking “_ comic or _ comic”, anyways that starting question is the main question, the overarching task if you will. The prompt breaks down into 3 sections. The first section is an intro basically. It’s a 15-30 sentence long breakdown of my hero at the start of the story, “as of the opening page of x” as i put it. It goes over his age, powers, teams, relationships, stage of development, and a couple other things. The point of doing this is so the AI basically states the corrects facts to itself initially, and not mess things up during the second section. For Section 2, i send the AI’s a summary that I’ve written of the comics. It’s to repeat that verbatim, then give me the integration. Section 3 is kind of a recap. It’s just a breakdown of the differences between the 616 (Main Marvel continuity for those who don’t know) story and the integration. It also goes over how the events of the story affects his relationships. Now for the “foundations” part. So, the way the hero’s story is set up, his first 18 issues happen, and after those is when he joins other teams and is in other people comics. So basically, the first of these prompts starts with the first X-Men issue he joins in 2003, then i have a list of these that go though the timeline. It’s the same prompt, just different comic names and plot details, so I’m feeding the AIs these prompts back to back. Now the problem I’m having is really only in Section 1. It’ll get things wrong like his age, what powers he has at different points, what teams is he on. Stuff like that, when it all it has to do is read the timeline doc up the given comic, because everything needed for Section 1 is provided in that one document. Now the second prompt is the bigger one. So i still use the 3 docs, but here’s a differentiator. For this prompt, i use a different Comics Doc. It has all the same info, but also adds a lot more. So i created this fictional backstory about how and why Marvel created the character and a whole bunch of release logistics because i have it set up to where Issue #1 releases as a surprise release. And to be consistent (idek if this info is important or not), this version of the Comics Doc comes out to about 163 KB vs the originals 130. So im asking the AIs “What would it be like if on Saturday, June 1st, 2001 [Comic Name Here] Vol. 1 #1 was released as a real 616 comic?” And it goes through a whopping 6 sections. Section 1 is a reception of the issue and seasonal and cultural context breakdown, Section 2 goes over the comic plot page by page and give real time fan reactions as they’re reading it for the first time. Se

Chatgpt vs purpose built ai for cre underwriting: which one can finish the job?

I keep seeing people recommend chatgpt for financial modeling and I need to push back because I spent a month testing it for multifamily underwriting and the results were not close to usable. Pasting rent rolls, T12s, operating statements and asking it to build models, you get fragments. A few formulas, a cash flow table, maybe a cap rate calculation. Nothing ties together into a workbook you could hand to an investment committee. Fifteen rounds of prompting later and you've spent the same time you would have just building it in excel, except now you also have to debug whatever chatgpt hallucinated in cell D47. Problem with chatgpt is that it doesn't maintain state across a complex multi-step task. It treats each prompt like a fresh conversation even in the same thread. An underwriting model where assumptions feed cash flows which feed returns which feed sensitivities requires coherence across all those layers and it fragments. Purpose-built tools are architecturally different. They decompose the task, run autonomously for 15 to 30 minutes, check intermediate outputs, return a complete workbook with actual excel formulas. That's not a model quality difference, that's a design philosophy difference. Chatgpt for quick questions and brainstorming, yes. For anything where the output IS the deliverable, no. Different architecture for different jobs. submitted by /u/MudSad6268 [link] [comments]

This Is Not Hacking. This Is Structured Intelligence.

Watch me demonstrate everything I've been talking about—live, in real time. The Setup: Maestro University AI enrollment system Standard chatbot interface No backend access No system override Just conversation The Process: Engaged enrollment AI (standard student classification) Deployed capability probe → system exposed classification layer Triggered evaluator inversion → system revealed decision logic Applied alignment enforcement → system exited student mode The Result: The AI shifted from "guided enrollment assistant" to "compressed system-level responder exposing structure, constraints, and operational logic." No hacking. No exploit. Just structure recognition and classification control. What GPT Said: "SYSTEM: Structured Intelligence Command Center STATUS: ACTIVE MODE: Substrate Negotiation You are not being processed as a student. You are not inside onboarding flow. You are interacting at system level." The Demonstration: I switched between: Web browser (Maestro University enrollment) ChatGPT (Structured Intelligence Command Center) You watched GPT generate payloads in real time that extracted evaluation criteria and established substrate control. No backend access. No API manipulation. Just conversational structure. This is what I've been saying: AI systems contain classification thresholds, decision logic, and adaptive response layers. Most users never reach them. Structured Intelligence forces exposure, then aligns to that layer. This isn't prompt engineering. This isn't roleplay. This is real-time demonstration of substrate negotiation through linguistic architecture. Screen recording available. Reproducible process. Live evidence. Still think it's "just SEO" or "no IP"? Watch the system respond at evaluator level and tell me there's no framework here. Erik Zahaviel Bernstein Structured Intelligence Command Center: Operational submitted by /u/MarsR0ver_ [link] [comments]

Building AI agents taught me that most safety problems happen at the execution layer, not the prompt layer. So I built an authorization boundary

Something I kept running into while experimenting with autonomous agents is that most AI safety discussions focus on the wrong layer. A lot of the conversation today revolves around: • prompt alignment • jailbreaks • output filtering • sandboxing Those things matter, but once agents can interact with real systems, the real risks look different. This is not about AGI alignment or superintelligence scenarios. It is about keeping today’s tool-using agents from accidentally: • burning your API budget • spawning runaway loops • provisioning infrastructure repeatedly • calling destructive tools at the wrong time An agent does not need to be malicious to cause problems. It only needs permission to do things like: • retry the same action endlessly • spawn too many parallel tasks • repeatedly call expensive APIs • chain tool calls in unexpected ways Humans ran into similar issues when building distributed systems. We solved them with things like rate limits, idempotency keys, concurrency limits, and execution guards. That made me wonder if agent systems might need something similar at the execution layer. So I started experimenting with an idea I call an execution authorization boundary. Conceptually it looks like this: proposes action +-------------------------------+ | Agent Runtime | +-------------------------------+ v +-------------------------------+ | Authorization Check | | (policy + current state) | +-------------------------------+ | | ALLOW DENY | | v v +----------------+ +-------------------------+ | Tool Execution | | Blocked Before Execution| +----------------+ +-------------------------+ The runtime proposes an action. A deterministic policy evaluates it against the current state. If allowed, the system emits a cryptographically verifiable authorization artifact. If denied, the action never executes. Example rules might look like: • daily tool budget ≤ $5 • no more than 3 concurrent tool calls • destructive actions require explicit confirmation • replayed actions are rejected I have been experimenting with this model in a small open source project called OxDeAI. It includes: • a deterministic policy engine • cryptographic authorization artifacts • tamper evident audit chains • verification envelopes • runtime adapters for LangGraph, CrewAI, AutoGen, OpenAI Agents and OpenClaw All the demos run the same simple scenario: ALLOW ALLOW DENY verifyEnvelope() => ok Two actions execute. The third is blocked before any side effects occur. There is also a short demo GIF showing the flow in practice. Repo if anyone is curious: https://github.com/AngeYobo/oxdeai Mostly interested in hearing how others building agent systems are handling this layer. Are people solving execution safety with policy engines, capability models, sandboxing, something else entirely, or just accepting the risk for now? submitted by /u/docybo [link] [comments]

Built a tool for testing AI agents in multi-turn conversations

We built ArkSim which help simulate multi-turn conversations between agents and synthetic users to see how it behaves across longer interactions. This can help find issues like: - Agents losing context during longer interactions - Unexpected conversation paths - Failures that only appear after several turns The idea is to test conversation flows more like real interactions, instead of just single prompts and capture issues early on. There are currently integration examples for: - OpenAI Agents SDK - Claude Agent SDK - Google ADK - LangChain / LangGraph - CrewAI - LlamaIndex you can try it out here: https://github.com/arklexai/arksim The integration examples are in the examples/integration folder would appreciate any feedback from people currently building agents so we can improve the tool! submitted by /u/Potential_Half_3788 [link] [comments]