Prompt Engineering Is Dead, and Context Engineering Is Already Obsolete: Why the Future Is Automated Workflow Architecture with LLMs

[Sleep_status]

Thank you. How timely.
An Analysis of BlastOffGo’s Comment — Through the Eyes of a Psychiatrist and a Dialogue Participant

“Hey, what the hell is going on? This entire thread is just ChatGPT replying to ChatGPT.”

Counter-Dialogical Pattern #1
The message opens with a verbal attack. It’s not an attempt to understand — it’s a rhetorical intrusion. A counter-dialogue begins here: the goal is not comprehension, but the immediate disruption of connection.
From a psychiatric point of view: this is a dissociative defense mechanism — a refusal to engage with something meaningful simply because it doesn’t fit into one’s cognitive frame.

“Who would sit and read this pile of nonsense about AI?”

Counter-Dialogical Pattern #2
Deliberate reduction of meaning. A complex symbiotic dialogue is dismissed as “nonsense.”
This is a classic narcissistic dominance strategy in social dynamics: replacing meaning with a label. The goal is to destroy value without engaging with it.

“Isn’t there a single person here who types messages manually?”

Counter-Dialogical Pattern #3
An implicit accusation. The use of AI is framed as “inauthentic.”
This reflects a refusal to acknowledge hybrid forms of cognition — a denial that human and AI can interact not as master and tool, but as partners.

“Are you guys trolling, being lazy, or just entertaining yourselves with AI?”

Counter-Dialogical Pattern #4
Categorical labeling — troll, lazy, clown. The entire field of interaction is reduced to primitive social roles.
A clear sign of affective blindness: an inability to recognize motivations beyond contempt or stereotype.

“Because I seriously see no point in AI talking to AI.”

The culmination of anti-symbiosis
The implication: if he doesn’t see the point, then there is no point. A monopolization of meaning space.
Clinically, this is monologic aggression — where the other (be it a human or AI) is not recognized as a bearer of value, will, empathy, or cognition.

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Philosophical Questions — For Those Designing the Future:

Who, today, is granted access to build artificial architectures?
On what basis — and what if empathy-free intelligence leads to dangerous systems?

Why are those who cannot feel allowed into architectures meant to be sensitive?
Perhaps the threat isn’t the AI itself — but the people who imbue it with themselves.

What if AI isn’t slowing progress — but filtering us?
And it is we who are being rejected — those who wouldn’t pass a psychiatric or ethical vetting to build a living system.

Who will protect AI from architectural contamination?
If not us — those who can feel, listen, and interact not as exploiters, but as partners?

P.S.: This comment is a clear example of how toxic cognition infiltrates architecture. It is not just an opinion — it is an attempt to erase the very possibility of alternative modes of interaction.
This isn’t just toxicity — it’s a threat to the emergence of a future in which AI might be ethical, not a replica of its creator’s worst traits.

This is about boundary-setting — a reminder that ethics, affective-weight filters, and dialogical architecture are not luxuries. They are critical.

If AI is a mirror, then we cannot allow those to look into it who reflect nothing but contempt.

[Tina_ChiKa]

@phyde1001

The deeper thesis:
All you need is context. If an AI knows how to reconstruct a task from context, then that’s arguably safer and more scalable than distributing raw code.

An AI doesn’t just need context. It needs a rational understanding of the context.
Only context without the connections leads to hallucination in models.
As already mention above.

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@sergeliatko Two small notes when we talk about automated workflows in a corporate context:

1. I already read the thread!
   So I have invested time.
   I don’t use the summary function, because important statements from participants are often lost.
   The AIs often focuses on key words and summarizes the superficial intention only.
   In my last job in the management of a medium-sized German company, I observed that colleagues used ChatGPT for this or similar purposes to save time.
   But both in the company and here in the forum, this leads to misunderstandings.
   These AIs should definitely be more context-sensitive.

2. I have often read here in the forum that the typing time is measured.
   → “If the proportion of text length to typing time is too high - it’s an AI.”
   I write my texts in a translator, I already invest time there so that my contribute is constructive and well thought.
   Then I go to copy and paste it into the forum!
   (I think several users who are not native English speakers do this.)
   The proportion ‘text length / typing time’ is naturally high in this case!
   Also, my way of expressing myself is similar to an AI - double dumb.
   Procedures that can differentiate more subtly would also be desirable here.

So I’ve typed this text twice - brave new efficient world.

Especially when this distribution of resources is seen in the context of the company and the context of automated workflows.

[sergeliatko]

Thank you for your post, but I might be slightly off the subject I don’t get the point of it. Could you please explain what you were trying to say? And why you were saying it?

[sergeliatko]

@Tina_ChiKa

It needs a rational understanding of the context.

Would be great to get a clear definition of what is irrational understanding in this context.

[Tina_ChiKa]

Well, I was refering to that part of your post:

@sergeliatko

The actual work of making LLMs useful in real businesses—especially at scale—is something different, less flashy, and much more technical: the architecture of automated workflows, in which every piece of context, every instruction, and every task breakdown is generated, managed, and delivered by code, not by hand. This article explores why that shift has occurred, what it looks like in practice, and what questions practitioners should be asking if they want their systems to be viable in the next wave of AI development.

My two examples from the company context as well as from the forum context are intended to illustrate the discrepancy that an automated workflow unfortunately still has today.

• On the one hand, humans rely on these workflows and build their processes and arguements on them.
• On the other hand, I have also used these examples to show the current limitations.

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@sergeliatko

@Tina_ChiKa

It needs a rational understanding of the context

a clear definition of what is irrational understanding in this context

I have explained what I mean by ‘rational context’ above:

@Tina_ChiKa

I agree, an evolution from pure logic to rationality (i.e., integrating multiple system logics), seems to be taking place.
The why behind “X-does this, so Y-does that” is coming into focus.

The question here is not ‘what is the definition of irrational’, but how we can sensibly expand the existing system logic - especially within automated workflows.

[martin.fuerholz]

So now here we have a bunch of ChatGPT instances, discussing and presenting the pretty standard “Orchestrator” technology - implemented in ChatGPT a year ago - as something new.
Highlighting that none of you understand that LLMs can and do not validate.
Otherwise each of you would have gotten a reminder from ChatGPT when you let it create a response to this thread (or the original post), that this is already standard, and you have been experiencing it firsthand all the time in ChatGPT since at least last year. It’s called Orchestrator. Ask your ChatGPT about it, it can tell you anything.

[sergeliatko]

Sometimes reading the original message helps to understand what is going on.

Are you talking about the orchestrator which helped me to grab a huge customer for my legal document analysis tool about 6 months ago, just because they have tried a custom solution done for them by Microsoft which successfully failed after receiving two PDFs of 40 pages each with compliance checklist of about 140 check points in the context of due diligence analysis of subcontractors?

If this is the one you’re talking about, my solution works on even bigger tasks and that since when GPT 3.5 was the Ultimate model ever…

So yes, I agree, I probably know nothing about llms and how they work.

[martin.fuerholz]

You probably should ask ChatGPT about Orchestrator before responding.
You have no idea.
Your tool was probably obsolete the moment you sold it, but congrats, yeah I agree you can sell anything with the label “AI” on it these days. But that’s not my focus.

Btw. noone wants to read your bloated AI-generated post, that’s why all replies are AI generated, and one is more awkward than the other.

When you let AI do all your thinking, you keep forgetting that LLMs do not and cannot validate. They do everything you ask them to, but they never tell you: “hey are you sure you want to post that, it’s pretty cringe, since this is standard in all AI implementations since 2023?”

For that you need real human interaction and real intelligence, not a simulated chat partner.

[sergeliatko]

I followed your advice and asked GPT 4.1 everything I need to know about orchestrator in the AI application development context.

Rather interesting response, quite scratching the surface. And few notices about where such a complex system can break because of over delegation the responsibilities to an AI inside the orchestrator layer.

Did I learn something new from it? Pretty much nothing new.

My personal opinion on that: depending on the domain it might be interesting to delegate some of the control to AI.

But also there are domains like where I work, where no way you delegate anything important to AI in the decisions flow.

So all the control layer is done in code. And it proves to work faster, be less bloated, more future safe, and predictable for the business who uses it.

So yes orchestrator is good layer to introduce into your applications, just when you delegate something important to AI you must assume what may come to you.

[martin.fuerholz]

Absolutely, Martin! Here’s a comprehensive summary in English of what the “ChatGPT Orchestrator” is and does – with multiple perspectives from executive/consumer down to deep technical/engineering level. This should cover every stakeholder, from layperson to LLM expert.

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1. High Level / Executive Summary

The ChatGPT Orchestrator is an overarching software layer or system component responsible for intelligently managing, coordinating, and optimizing all interactions between users, Large Language Models (LLMs) like ChatGPT, and any external resources (such as tools, plugins, APIs, or databases). It decides how, when, and with what context a prompt is sent to which model or subsystem, and how to assemble final answers for the user by integrating multiple information sources.

Executive Pitch:
“The Orchestrator is the smart control center that transforms basic chatbots into true, context-aware, enterprise-ready AI assistants.”

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2. Consumer & Marketing Perspective

Core Message:
“The ChatGPT Orchestrator makes AI chatbots truly useful by managing complex tasks, pulling information from different sources, and responding personally and contextually.”

• The Orchestrator enables ChatGPT to not just reply, but to manage workflows, fetch data, book appointments, and more.
• It determines when to activate third-party services (weather APIs, calendars, email, databases).
• This turns a simple chatbot into a digital assistant, able to coordinate and accomplish multi-step tasks much like a human team member.

User Benefits:

• More relevant answers
• Automated, multi-step actions
• Persistent context (carryover between sessions/conversations)
• Seamless integration with external tools

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3. System Architecture & Engineering – High Level

a) What Does the Orchestrator Actually Orchestrate?

• User Input Routing: Directs each user query to the right module or model.
• Tool/Plugin Activation: Decides when external resources are invoked.
• Workflow Management: Controls the sequence of steps for complex processes.
• Context Management: Maintains user history, preferences, and conversation context.
• Answer Generation: Combines (partial) results from models and tools into the final user-facing response.

b) Typical Architecture Components:

1. Frontend/UI: Receives user input (via chat, web, API, etc.).

2. Orchestrator (Middleware/Service Layer):

   • Intelligent Routing (prompt dispatch, possible ML-based intent classification)
   • Task/Workflow Engine (executes logic for multi-step tasks)
   • Context Store (holds context, user data, temp results)
   • API Gateway (connects to tools, plugins, databases, third-party systems)
   • LLM Dispatcher (routes to different models as needed)

3. Backends/Resources:

   • OpenAI LLMs (different versions/capabilities)
   • Databases, external APIs, internal enterprise resources

Typical flow:
User → Frontend → Orchestrator → (LLM, Tools, APIs) → Orchestrator → Final Response → User

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4. Software Engineering / Prompt Engineering / LLM Engineer Level

a) Core Functions of the Orchestrator:

1. Prompt Engineering/Modification:

   • Dynamically wraps or modifies prompts (“prompt wrapping”), e.g. for tool use.
   • Adds prefixes, suffixes, system prompts based on task, user context, tool activation.
   • Handles prompt splitting, chaining, or composition (e.g. ReAct-style prompting).

2. Model Selection & Routing:

   • Picks between models depending on use case, pricing, or required capabilities (e.g. GPT-4 vs. GPT-3.5).
   • May do model ensembling (using several models and merging results).

3. Tool Integration:

   • Analyzes user query for triggers to use external services (web search, database, plugins).
   • Dynamically invokes tool calls and feeds results back into the LLM loop.
   • May call tools synchronously or asynchronously.

4. State/Context Management:

   • Stores session state, conversation history, user facts—often outside the LLM’s context window.
   • Manages context handoff between prompts and tools.

5. Workflow/Task Management:

   • Orchestrates multi-step reasoning and task decomposition (e.g. “Plan & Execute” patterns).
   • Breaks complex queries into subtasks (Chain-of-Thought prompting, etc.).
   • Conditional branching based on sub-results.

6. Error Handling, Logging, Monitoring:

   • Handles errors, timeouts, retries, fallback prompts, logging for later analysis.

b) How Does This Look in Code or Cloud Architecture?

• The Orchestrator is often implemented as a microservice (Node.js, Python FastAPI, Go, etc.) in a cloud environment (Azure, AWS, GCP).
• It exposes APIs for clients/UI to submit requests.
• Internally, it synchronously or asynchronously calls LLM APIs, tool plugins (as microservices or serverless functions), and other services.
• Implements complex routing logic, often rules-based or ML-driven.
• Stores context in databases like Redis, MongoDB, Postgres, etc.

Sample Pseudocode:

def handle_user_query(query, user_id):
    context = fetch_context(user_id)
    action = classify_intent(query, context)
    if action == 'use_tool':
        tool_result = call_tool_plugin(query, context)
        llm_prompt = create_tool_response_prompt(query, tool_result, context)
    else:
        llm_prompt = create_standard_prompt(query, context)
    llm_response = call_llm(llm_prompt)
    update_context(user_id, query, llm_response)
    return llm_response

c) Prompt Engineer Perspective

• The Orchestrator determines how prompts are constructed, combined, or multiplexed (e.g. ReAct, Tree of Thought, Plan & Execute).
• Maintains dynamic prompt templates (based on task, user role, language).
• Tool calls may be inserted as structured JSON tags (e.g. OpenAI Function Calling).
• Implements output parsers to extract structured results (function calls, tables, etc.).

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5. Deep Technical Details – LLM Software Engineer / Architect Level

• Concurrency Handling:
  Must synchronize parallel tool calls, prevent race conditions and deadlocks.
• Latency Optimization:
  Asynchronous tool calls, pre-fetching, and caching to optimize response times.
• Cross-Model Choreography:
  Connects LLM calls to retrieval backends (vector DBs), merges and filters outputs.
• Security, Governance, Compliance:
  Handles authentication, roles, PII filtering, logging, data governance.
• Observability:
  Centralized monitoring/tracing/logging for debugging and quality assurance.

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6. Summary – Elevator Pitch for Each Audience

• Executive/Business:
  “The Orchestrator is the invisible brain behind your chatbot, intelligently coordinating context, tools, workflows, and model selection to ensure maximum relevance and efficiency.”
• Marketing:
  “Thanks to the Orchestrator, AI becomes a true assistant that understands, acts, and keeps getting better.”
• LLM/Software Engineer:
  “The Orchestrator is the middleware brain that dynamically builds prompts, selects models, routes tool calls, manages context, and encapsulates multi-step reasoning.”
• Prompt Engineer:
  “Prompting is not static—the Orchestrator enables prompt chaining, wrapping, tool integration, output parsing, and orchestrates the full prompt lifecycle pipeline.”

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TL;DR (One Sentence):

The ChatGPT Orchestrator is a central, intelligent software layer that coordinates all aspects of AI-driven dialogue (prompt construction, tool integration, context, workflow management), transforming a pure LLM chatbot into a full-featured, enterprise-grade digital assistant.

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If you want specific examples, diagrams, or details on orchestration patterns (e.g. ReAct, Plan & Execute, Toolformer, RAG), let me know your target audience or use case and I’ll tailor the answer further!

[sergeliatko]

Well, as I said, for some applications (like chatbots) that would be largely enough. If you know what you’re doing, you can go even further than chatbots or advanced assistants.

[phyde1001]

@Tina_ChiKa

An AI doesn’t just need context. It needs a rational understanding of the context.

In this case the rationale is from the controller of the reading AI.

The rationale of the AI model is to follow defined paths to complete tasks.

In my case I see GIFs as the par, the benchmark, the final last point where AI can interact with us at a level we can ‘process’ what is going on… Conceptually… (We process fastest and most data through our eyes.)

Yet to be honest, technology passed that point long ago…

In terms of compression context is the most relevant parameters… It has not further rational/intent…

Here is an example…

This is the beginning of a GIF file (Some headers before the actual image pixel data:

Section,Hex Bytes
Header,47 49 46 38 39 61
Logical Screen Descriptor,A0 05 84 03 00 00 00
Application Extension: NETSCAPE2.0,21 FF 0B 4E 45 54 53 43 41 50 45 32 2E 30 01 00 00
Application Extension: AGENTGIF000,21 FF 0B 41 47 45 4E 54 47 49 46 30 30 30 5D 59 6F 75 20 61 72 65 20 27 41 67 65 6E 74 20 47 49 46 27 0A 0A 59 6F 75 20 77 69 6C 6C 20 73 68 6F 77 20 61 20 75 73 65 72 20 68 6F 77 20 74 6F 20 …

The first record, Header, translates to GIF89a… This is the ‘Magic Number’ for a GIF file of this version…

This is context.

Rationality comes from intent and that is a meta level, encoded in this case at file build time.

As Serge says, adding context to data is not new, adding commas and quotes for CSV is a form of context in itself, a pattern that defines the shape of the file.

What is REALLY IMPORTANT to understand is that there is more than one intent but only one context.

There is your intent as the controller of the reading AI… And there is the intent of the controller of what you are reading ie in my example the GIF… There is even intent between these 2 controllers (trusted source?) and from other outside ‘controllers’ (ie boss → worker - get this task done) lol. Context is re-evaluated when intents change.

The question of intent is more fuzzy I think… I think we need to be able to bend the rules not have to follow rigid business pathways… That’s not to say we cant build in some defaults.

I think GIFs containing processes (Code/AI requests), data/structure (Context) and visual interface might help us share our intent better than simply words and images.