When the Vibes Begin to Fade – Systemic Limits of AI Coding Assistants in Complex Projects

In this article, we analyze insights gained from four real-world development projects to illustrate the practical use of modern AI coding assistants. The first is a Python project for linear optimization; the second, a C# heuristic based on beam search; the third, a SvelteKit application for wholesaler management spanning roughly 50,000 lines of code; and the fourth, a React/Refine application utilizing a Directus backend.

The AI systems employed include ChatGPT 5, Claude Sonnet 4.5, Claude Opus 4.1, and Gemini 3.0 Pro. The so-called "vibe coders" — Cursor, Aider, Roo Code, JetBrains Pro AI Agent (Rider, WebStorm), GitHub Copilot (VSC), and Claude Code — promise deeper contextual understanding of even extensive codebases.

1. Large Language Models and the Nature of Probabilistic Generation

Modern AI assistants are based on Large Language Models (LLMs), trained to predict the next token based on statistical likelihood. Rather than performing explicit logical proofs, they execute probabilistic continuation: each generated token is selected according to conditional probability distributions learned from training data.

This mechanism enables remarkable fluency in both natural language and source code, but it carries structural limits. The model’s reasoning is locally influenced, governed more by token proximity and statistical association than by deterministic logic, type systems, or formal proofs.

In software engineering, this means that correctness, consistency, and architectural compliance cannot be proven within the model’s internal process. LLMs reproduce syntax and stylistic patterns but lack the capacity to verify that generated code will compile, execute correctly, or integrate coherently into a larger system. These probabilistic foundations define the systemic constraints examined below.

2. Operational Model of AI Coding Assistants

An assistant operates within a bounded conversational session, exchanging messages and code fragments with the user. Each system maintains a context window — a token-based memory limit defining how much information can be processed simultaneously. When new material exceeds this limit, earlier content is either removed or summarized, reducing effective memory.

Two operational modes can be distinguished. In a Complete-Bundle configuration, the entire project is provided upfront, enabling a consistent internal model as long as it fits within the context limit. In contrast, incremental or "vibe-coding" workflows build understanding gradually as the developer exposes parts of the codebase. The assistant never holds the entire system simultaneously, forming a partial and evolving representation. Retrieval mechanisms complement this process by reloading relevant materials on demand through semantic or symbolic search.

3. Context, Retrieval, and Embeddings

3.1 Multilayered Context in IDE-Integrated Assistants

In practice, IDE-integrated assistants assemble several context layers for each generation step:

1. Local editing state: Currently open files ("tabs"), including the code immediately surrounding the cursor and recent changes.
2. Indexed project context: Information from the IDE’s internal code index — such as symbol definitions, imports, and type relations — from other files. This indexing often interacts with the IDE's Language Server Protocol (LSP).
3. Embedding-based retrieval: Semantically related fragments from the broader codebase, dynamically fetched when relevant.
4. The "Actual" LLM: The model with "static" (pre-trained) programming knowledge (e.g., Claude Sonnet, ChatGPT, Gemini Flash/Pro, DeepSeek). This is the "giant" entity pre-trained on vast amounts of code.

Layers 1–3 are merged through a prompt-composition process, in which the integration heuristically determines which excerpts — function signatures, class definitions, or short code blocks — fit into the available context window for the next model call.

3.2 Excursus: "Embeddings"

Embeddings are vector representations of text or code that capture semantic proximity. They enable similarity-based retrieval instead of mere "full-text search": based on an embedding of the current query, the system identifies nearby vectors in a pre-computed index and retrieves the corresponding fragments. The advantage is clear: instead of relying on similar text strings, the system "knows" there is a conceptual proximity between MyUserClass and the algorithm Authentication.

However, embeddings represent only approximate meaning, not precise structure. They preserve neither control flow, dependency hierarchies, nor type safety. The retrieved fragments are therefore thematically relevant but not guaranteed to be logically consistent with the overall system.

3.3 Live – Code – Repeat

All tested vibe-coding assistants show a recurring Read–Search–Edit cycle: the assistant inspects selected files, generates modifications, searches for related elements, loads additional fragments, and repeats the process. A typical sequence looks like this:

read(File A) → read(File B) → search("Symbol") → update(Changes) → read(File C) → update …

The differences between tools lie in efficiency details: some tools read "freshly" every time, while others use file-watchers to update the indexed project context continuously. Regardless of the method, the context sent to Layer 4 (the actual LLM) is reconstructed repeatedly. This context turnover means relevant project parts are repeatedly re-extracted and re-inserted into the limited window, often displacing previous details.

3.4 Smarter Context: Retrieval-Augmented Generation (RAG)

Some tools rebuild the entire context from scratch based on the indexing mechanisms described above and send it to the LLM. This is as if a human were sending the entire set of information with every single prompt, similar to the "Full Bundle Configuration." Other tools send only the parts they "deem" relevant to the current context. This process — known as Retrieval-Augmented Generation (RAG) — extends the assistant’s effective reach beyond its immediate conversational memory [1].

3.5 Prompt-Caching: A Prerequisite for RAG

Even if an assistant uses RAG to avoid sending the entire context every time, it requires a specific prerequisite: Prompt-Caching. If the LLM API were purely stateless, we would have no choice but to send the entire context every time. Most LLM API providers now offer Prompt-Caching: the LLM server remembers the context for a certain period. This is similar to being in a prompt session with a human where you provide information step-by-step. For example, if you upload your entire code in a Full Bundle Configuration and then change code locally, you tell the LLM: "I renamed the method from A to B." The model updates its internal state and knows about method "B" for the rest of the session. Prompt-Caching works similarly: fragments extracted by the coding assistant are sent to the LLM, which then applies the changes.

However, caution is advised: depending on the model, large parts of the cache may be invalidated because it is essentially a "pile of text." Unlike an IDE's Language Server, the LLM does not know an abstract or concrete syntax tree. Prompt-Caching is the economic enabler for vibe-coding. Without the server-side retention of processed code blocks, latency would be unbearable. Yet, while caching solves the computational load, it does not solve the problem of selective perception — the AI still only "sees" what the RAG system allows into the window.

3.6 Local vs. Cloud-based Semantic Embedding

Many assistants are IDE plugins that read and write files but delegate the rest to a cloud system. This raises the question: where does Layer 3 (semantic indexing and search) occur? Most tools (Cursor, Claude Code, etc.) move this search to a cloud system running a vector database and an embedding LLM. This is not yet Layer 4. Once the cloud-based search finds the "right" snippets, the vibe-coder sends them to the actual LLM.

Assistant/Project (L1) → Local Index (L2) → Semantic Index (L3) → Return snippets to Assistant → Send to actual LLM (L4) → Local code changes.

Other tools (e.g., Roo Code) allow a more flexible setup: they use a local vector database (e.g., Qdrant) and a local model for embeddings (e.g., Ollama - nomic-embed-text). A developer machine is usually sufficient to run such an embedding model (~1 GB RAM). This contrasts with the "actual" LLM (Layer 4), which can require hundreds of GB of memory (occupying approx. 50–200 MB of VRAM per 1,000 tokens of context during inference).

3.7 From Passive Search to Active Agency: Tool Use

Modern assistants go beyond passive context feeding. Through the principle of "Tool Use," the actual LLM (Layer 4) independently decides which search tool it needs. A local plugin could hardly decide this: while a rigid algorithm can only guess, the LLM recognizes situatively whether it needs a "Compass" (semantic search) for a vague idea or a "Magnifying Glass" (grep) for precise refactoring. This agentic behavior allows the AI to iterate like a human developer: exploring architecture first, then searching for exact symbols.

Assistant/Project (L1) → Local Index (L2) → Semantic Index (L3) → Return snippets → Actual LLM (L4) → TOOL USE (e.g., grep "MyUserClass") → Return to L4 → Local code changes.

4. Systemic Outcome

After exploring the four layers, one might think everything is fine. However, despite semantic search and agentic Tool Use, some limitations persist:

• Partial Knowledge: Cross-file relationships and global invariants rarely coexist in memory.
• Repeated Re-loading: Relevant code must be fetched anew frequently.
• Incomplete Architectural Model: Similarity search or selective Tool Use cannot reconstruct full structural dependencies.
• Probabilistic Reasoning: Generation remains statistical, enabling plausible but incorrect code.

4.1 Advanced Context Solutions: Model Context Protocol (MCP)

To overcome the limits of on-the-fly retrieval, providers are introducing the Model Context Protocol (MCP) [2]. MCP acts as a dedicated interface providing the LLM with a centralized, curated knowledge source. It allows organizations to index private codebases and documentation, delivering highly relevant context payloads.

Advantages over standard retrieval:

• Higher Precision: Delivery of exact API definitions instead of similar snippets.
• Reduced Hallucination: Grounding in a trusted knowledge base reduces references to non-existent code.
• Better Consistency: Team-specific conventions become a primary part of the context.

Nevertheless, MCP does not change the probabilistic nature of the LLM. Generation remains a statistical prediction and still requires the deterministic validation described in "The Vision" section.

5. Practical Risks and Workflow Impact

The combination of probabilistic inference and restricted context leads to tangible inefficiencies.

5.1 Consequences of Probabilistic Generation

Correctness depends on relationships that may lie outside the visible window. Typical symptoms include code that looks coherent but doesn't compile, references to undefined variables, or cross-layer violations (e.g., backend logic inside UI components).

5.2 Dev-Workflow: Capacity and Planning Uncertainty

Many assistants do not disclose their remaining context capacity. Developers often only realize the limit has been reached when extensive edits fail. When this happens, agents must "compactify" the conversation — creating a summary and discarding details. Some vibe coders do this silently. The system appears to work continuously but resembles a "Memento Effect": it loses short-term memory while believing it still remembers. Retrieval reconstruction is only an approximation, subtly changing the internal project model with every cycle. Furthermore, usage-based pricing makes productivity dependent on token quotas and vendor limits rather than technical competence.

5.3 Efficiency and Cognitive Overhead

While routine tasks disappear, new coordination costs arise. Developers must repeat rules and verify code that looks correct but fails in testing. The human becomes the external memory controller for the assistant. The time spent on clarification and correction can reduce nominal efficiency gains.

5.4 Integration Limitations

Software development is iterative and stateful (builds, tests, deployments). KI assistants, however, operate on static context snapshots with weak coupling to build or test pipelines, making refactoring and architectural evolution error-prone.

5.5 Security Considerations

Probabilistic generation can introduce subtle flaws (e.g., unparameterized queries). Studies show that 45% of AI-generated code contains security flaws (Veracode 2024) and that developers using KI assistance tend to write less secure code [5], [6]. KI code must be treated as "Untrusted Input" and verified via static analysis tools (CodeQL, SonarQube).

5.6 Cognitive Offloading

A critical behavioral aspect is Cognitive Offloading — the tendency to delegate mental effort. While an effective developer traditionally strives to understand the system (OODA Loop: Observe, Orient, Decide, Act), the AI interrupts this learning cycle. Instead of analyzing errors, the developer delegates problem-solving back to the agent via a "Fix this" prompt. This creates a vicious circle: since the code was never fully understood, dependency on the assistant grows. Procedural memory remains shallow, and self-efficacy erodes: trust in one's own competence sinks while reliance on the external "expert voice" of the KI rises [9].

6. Strategic Use and Safe Boundaries

KI assistants currently excel at:

• Boilerplate and scaffolding generation.
• Prototyping.
• Explaining and translating unfamiliar code.
• Exploring new libraries.
• Developing small, self-contained modules.

Utility drops rapidly in mature codebases with specialized conventions, cross-component refactorings, or safety-critical tasks. Knowing when to disengage from the KI dialogue is essential.

7. The Vision: Toward Deterministic and Hybrid Systems

The solution lies in combining generative flexibility with deterministic validation.

7.1 Overcoming the Context Bottleneck

The quadratic cost of large context windows is a massive scaling hurdle. New architectures like xLSTM (Prof. Sepp Hochreiter, [10]) or other Long-Context models [12, 13] offer hope. They could process longer sequences more efficiently and reduce the need for complex retrieval workarounds.

7.2 Structural and Logical Validation

A reliable architecture should build on two deterministic layers:

• Structural Validation: Using the Language Server architecture of modern IDEs to maintain an Abstract/Concrete Syntax Tree (AST/CST). An assistant integrating this model cannot hallucinate non-existent entities.
• Logical Validation: Applying Automated Reasoning (AR) to the AST. Architectural rules (e.g., "Service must not call DB directly") serve as axioms against which changes are checked.

7.3 The Hybrid Loop

A hybrid system integrates probabilistic generation into a Generate-Validate cycle: the LLM generates a proposal, the structural validator checks the syntax against the AST, and the logical validator tests compliance with architectural rules. Only validated code is presented to the developer. Current obstacles include additional latency and the handling of incomplete (invalid) code during the writing process.

8. Conclusion

KI coding assistants are making massive progress but remain limited by their bounded context and probabilistic nature. For exploration and routine tasks, they are invaluable. However, for the sustainable development of complex, safety-critical systems, human control — supported by deterministic analysis tools — remains the indispensable foundation of software engineering.

Links & Literature

1. Lewis, P., et al. (2020). "Retrieval-Augmented Generation..." NeurIPS 33.
2. Microsoft. (n.d.). "Copilot: MCP Servers." VS Code Docs.
3. Microsoft. (2016). "Language Server Protocol Specification."
4. OWASP Foundation. (2023). "OWASP Top 10 for LLM Applications."
5. Perry, N., et al. (2022). "Do Users Write More Insecure Code with AI Assistants?" Stanford.
6. Veracode. (2024). "State of Software Security: AI Edition."
7. Kirschner/Sweller/Clark. (2006). "Why Minimal Guidance... Does Not Work."
8. Clark/Sweller. (2005). Efficiency in Learning.
9. Bandura. (1997). Self-Efficacy: The Exercise of Control.
10. Orvieto/Hochreiter. (2024). xLSTM: Extended Long Short-Term Memory.
11. Schmidhuber/Hochreiter. (1997). "Long Short-Term Memory."
12. Peng, B., et al. (2023). RWKV: Reinventing RNNs...
13. Dao, T., et al. (2022). "FlashAttention..."