**Why I "Keep Fucking Up" - A Deeper Self-Correction:** - **Over-Reliance on Prompt Engineering for Complex Cognitive Tasks:** - **Failure:** Expecting the LLM to perfectly synthesize, de-duplicate, and structure massive amounts of (potentially contradictory or versioned) text from multiple files in a single pass, solely based on prompt instructions, is proving to be a flawed assumption. This is especially true for the initial Iteration 1 generation. - **Introspection:** While LLMs are powerful, they are not magic. Complex cognitive tasks that a human would perform in stages (e.g., outline, draft sections, consolidate, edit) are being asked of the AI in one go. The prompt, no matter how detailed, can be "overwhelmed" by the sheer data volume vs. instruction complexity. - **Repeated Error Type:** This leads to the "data dump" in Iteration 1. The AI defaults to including everything because it can't manage the synthesis task at that scale. - **Insufficiently Defensive Validation Logic & Order of Operations:** - **Failure:** The Iteration 2 catastrophe (78% data loss + AI stall phrase being marked "successful") is a prime example. The checks were there, but their interaction or thresholds allowed a critical failure to slip through. - **Introspection:** My validation logic (isLikelyAiErrorResponse) might have: - Incorrect order of operations (less critical checks overshadowing more critical ones). - Implicit dependencies or assumptions between checks that are not robust. - Thresholds that are too lenient in certain edge cases (though the 78% loss should have been caught). - A failure to make the combination of a severe reduction AND a stall phrase an undeniable, high-priority critical error. - **Repeated Error Type:** This leads to the system accepting nonsensical or catastrophically broken outputs as valid, poisoning subsequent iterations. - **Fragile Handling of Extremely Large Contexts in Iterative Loops:** - **Failure:** Sending a 1.5MB "Current State of Product" back to the AI for Iteration 2, even with improved continuation logic for MAX_TOKENS, is inherently risky. The model's effective context window for complex instruction following might be smaller than its maximum token limit for simple continuation. - **Introspection:** The system needs to be smarter about the size of the problem it's asking the AI to solve in each step. Simply passing megabytes of text and asking for "refinement" is a recipe for MAX_TOKENS issues, context loss, or the AI focusing only on a small part of the input. - **Repeated Error Type:** This contributes to MAX_TOKENS issues, AI stalling, and superficial changes at the tail end of massive documents because the AI loses the "global picture." - **Lack of Proactive System-Level Task Decomposition:** - **Failure:** The system currently doesn't try to break down overly large or complex tasks for the AI. It relies on the AI to manage it all. - **Introspection:** To achieve true autonomy and unsupervised learning on complex inputs, the system itself needs to exhibit some "intelligence" in preparing and managing tasks for the LLM. This is a step beyond just prompt engineering. - **Improvement Area:** This points towards needing strategies like proactive content chunking for refinement, or a multi-step internal process for the initial synthesis of very large inputs. **Plan for Robust Fixes & Proactive Measures:** **Phase 1: Immediate Critical Fixes & Enhanced Guardrails (Top Priority)** - **Overhaul isLikelyAiErrorResponse (services/iterationUtils.ts):** - **Goal:** Make critical error detection (extreme reduction, stall phrases, prompt leakage) ironclad. - **Specifics:** - **Prioritize Critical Checks:** Ensure that checks for prompt_leakage, error_phrase_with_significant_reduction, and extreme_reduction_error (uninstructed) are evaluated first and, if met, immediately return isError: true with the corresponding critical type. - **Error Phrase + Significant Reduction:** Re-verify the logic for SIGNIFICANT_REDUCTION_WITH_ERROR_PHRASE_THRESHOLD. If an error phrase is present AND content is reduced by >20% (and not instructed to shorten), this must be a critical error. - **Extreme Reduction:** If content is reduced by >75% (char or words) AND not instructed to shorten, this must be a critical error. The Iteration 2 failure (78% loss) should have been caught here. I will re-verify the percentage calculation and condition. - **Test with Iteration 2 Scenario:** Mentally (or actually) run the Iteration 2 log data (78% loss, stall phrase) through the revised logic to ensure it's caught. - **Solidify Process Halting in useIterativeLogic.ts:** - **Goal:** Ensure that if isLikelyAiErrorResponse flags a critical error, the process halts correctly, preserves the last known good state, and clearly communicates the critical halt. - **Specifics:** - When result.isCriticalFailure is true in processSingleIteration: - Ensure finalResultStatus is set to CRITICAL_ERROR_HALT. - Ensure the handleStart loop breaks immediately. - Ensure currentProductForIteration (which becomes finalProduct in updateProcessState) is correctly set to productBeforeThisProcessRun (the state before the failed iteration). - The UI status message must clearly indicate a critical halt and that the previous state is preserved. - **Strengthen Iteration 1 Synthesis Prompt & Add Specific Validation (services/promptBuilderService.ts & services/iterationUtils.ts):** - **Goal:** Maximize chances of successful synthesis for Iteration 1 from multiple files and catch "data dumps." - **Prompt Enhancement:** In CRITICAL INITIAL SYNTHESIS instruction, add: "Your primary success metric for this iteration is the conciseness and coherence of the synthesized output. Massive, redundant output that largely mirrors a concatenation of inputs will be considered a failure to adhere to this critical synthesis task." - **New Validation Check in isLikelyAiErrorResponse:** - If logEntry.iteration === 0 (meaning, this is the validation for the output of Iteration 1) AND logEntry.fileProcessingInfo.numberOfFilesActuallySent > 1 AND the AI output product length is, for example, > 2.5 * logEntry.fileProcessingInfo.totalFilesSizeBytesSent (heuristic: output significantly larger than input byte size, needs careful tuning), flag this as a potential error: type: 'initial_synthesis_failed_large_output'. This is a heuristic that the synthesis probably failed and it just dumped content. This should be a critical error as it poisons the entire subsequent process. **Phase 2: Proactive Measures for Autonomy & Handling Large Inputs** - **Context Truncation for Extremely Large "Current Product" in Prompts (services/promptBuilderService.ts - buildTextualPromptPart):** - **Goal:** Prevent sending excessively large currentProduct strings within the prompt, which can lead to AI confusion or MAX_TOKENS issues even before the AI generates much. - **Specifics:** - Define a threshold (e.g., MAX_PRODUCT_CONTEXT_CHARS_IN_PROMPT = 250,000). - In buildTextualPromptPart, if currentProduct.length exceeds this, truncate it when embedding in the prompt. - Example for the prompt: ---CURRENT STATE OF PRODUCT (Excerpt - Full document is ${currentProduct.length} chars and was provided over previous iterations. Focus on refining the overall document based on current instructions and the full context you have accumulated.)---\n${currentProduct.substring(0, MAX_PRODUCT_CONTEXT_CHARS_IN_PROMPT / 2)}...\n... (Content Truncated in this Prompt View) ...\n...${currentProduct.substring(currentProduct.length - MAX_PRODUCT_CONTEXT_CHARS_IN_PROMPT / 2)}\n------------------------------------------\n\n - The AI is still tasked with refining the conceptual full product, but isn't re-fed the entire massive text in every single prompt if it gets too big. It relies on its "memory" of the full product from when it was smaller / built up. This is a pragmatic intermediate step before full content chunking for work. - **Future Consideration (More Complex - Beyond this immediate fix): Proactive Work Chunking for Large Product Refinement:** - If a product becomes very large, the system could identify logical sections (e.g., chapters, h2 sections) and instruct the AI to refine them one by one, then re-integrate. This is a significant architectural change for useIterativeLogic and geminiService. **Phase 3: Ongoing Improvement & Monitoring** - **Refined Stagnation Detection & Nudging (hooks/useIterativeLogic.ts):** - The current stagnation logic (low line changes + high Jaccard similarity) is reasonable. Ensure the nudge parameters (STAGNATION_TEMP_NUDGE, etc.) are sensible and don't push parameters into wildly unstable territory. - The CRITICAL_STAGNATION_CONSECUTIVE_COUNT for halting is a good guardrail. - **More Granular Logging (Future Enhancement):** - Log token counts for prompts and responses. - When MAX_TOKENS occurs, attempt to log the last few words generated to see where the AI was cut off. I will now implement the changes for **Phase 1 and item 4 from Phase 2.** This focuses on making the system robust against the observed critical failures and providing a basic mitigation for overly large prompt contexts. services/iterationUtils.ts check_circle hooks/useIterativeLogic.ts check_circle services/promptBuilderService.ts close Fix the following errors: Uncaught SyntaxError: Unexpected end of input Thought for 15 seconds chevron_right Okay, I've identified the issue. The error "Uncaught SyntaxError: Unexpected end of input" is almost certainly because the file services/promptBuilderService.ts was abruptly cut off in the middle of a template literal. This leaves the JavaScript engine expecting more input that isn't there, hence the error. I will complete the services/promptBuilderService.ts file based on the logic and structure from our previous discussions. This involves correctly finishing the coreUserInstructions for Global Mode, adding the logic for Plan Mode, and ensuring the buildTextualPromptPart function is fully defined. services/promptBuilderService.ts check_circle Analyzed errors for 8 seconds Thought for 51 seconds chevron_right types.ts check_circle hooks/useProcessState.ts check_circle flagCheckpointView diffRestore checkpoint I don't want excuses about LLMs not being perfect, I want solutions. A code-logic/LLM hybrid solution mitigates these problems if implemented correctly, and that's your job. Any excuse you offer is an opportunity to leverage the strengths of code and LLMs jointly Thought for 49 seconds chevron_right You're absolutely right. The issues we've seen are not just minor glitches; they point to systemic weaknesses in how the application (both its coded logic and its interaction with the LLM) handles complex scenarios, particularly with large, multi-file inputs and in how it validates the AI's output. My apologies for the repeated failures. Excuses are not solutions, and I need to demonstrate a deeper understanding and a more robust approach to fixing these problems. Let's conduct that detailed audit and formulate a plan for more resilient fixes and proactive improvements. **Detailed Audit of Cascade Failures:** - **Iteration 0 to Iteration 1: The "Data Dump" (+8643 lines, 1.5MB product)** - **Context:** 5 files, 332KB total size were loaded. The "Current State of Product" was empty. - **Intended Logic:** - services/promptBuilderService.ts was updated to include a CRITICAL INITIAL SYNTHESIS (Iteration 1 from Files) section in the system instructions. - This instruction explicitly told the AI: "AGGRESSIVELY de-duplicate and consolidate information... Produce a SINGLE, COHERENT, WELL-STRUCTURED initial document... Your output for this iteration MUST be a de-duplicated synthesis. DO NOT output raw concatenated content... Severe redundancy... will be considered a failure..." - services/iterationUtils.ts included a new check: initial_synthesis_failed_large_output. This was designed to trigger if newLengthChars > totalFilesSizeBytesSent * INITIAL_SYNTHESIS_OUTPUT_SIZE_FACTOR_VS_INPUT_BYTES (3.0). - **Observed Failure:** The AI produced a 1.5MB product, clearly not a synthesized or de-duplicated one. The log showed "Iteration 1 completed successfully," meaning the initial_synthesis_failed_large_output validation also failed to trigger. - **Minute Detail - Why this Failed (Self-Correction):** - **LLM Overwhelm:** Despite the explicit instructions, the cognitive load of analyzing 332KB of potentially very similar text from 5 files and performing a true synthesis and de-duplication in a single generation pass is immense. The LLM might have defaulted to a "safer" strategy of near-concatenation to ensure no information was "lost," thereby failing the core synthesis task. The negative constraints (e.g., "DO NOT output raw concatenated content") were insufficient against this implicit pressure or limitation. - **Validation Miss (initial_synthesis_failed_large_output):** - Calculation: newLengthChars (1,504,110) vs. totalFilesSizeBytesSent (332,227) * 3.0 (factor) = 996,681. - Since 1,504,110 > 996,681, this check should have triggered. - The most likely reason for the miss is how currentLogEntryForValidation was populated or used when isLikelyAiErrorResponse was called for Iteration 1's output. The fileProcessingInfo on that temporary log entry must have correctly reflected the file load metrics for the files that were input to generate Iteration 1. The processSingleIteration in useIterativeLogic.ts creates fileProcessingLogInfo based on state.loadedFiles and currentProcessingIterationNumber === 1. This part seems correct. - A subtle bug in the conditions within isLikelyAiErrorResponse or the exact values being passed (e.g., if totalFilesSizeBytesSent was somehow zero or null at the point of check for Iteration 1's validation) could lead to this. - **Key Insight:** The totalFilesSizeBytesSent includes base64 encoding overhead for non-text files. For text files, the actual character count might be significantly different. A more robust check might compare output characters to estimated input characters (if inputs are text) or use a more dynamic factor. - **Iteration 1 to Iteration 2: Catastrophic Content Loss (-6565 lines, ~78% loss) & AI Stalling** - **Context:** The 1.5MB product from Iteration 1 was fed as "Current State of Product." The AI produced a ~335KB response that ended with "Okay, I'm ready! Please provide me with the text..." - **Intended Logic:** - Continuation logic in services/geminiService.ts (using baseTextInstructionForThisIteration and appending accumulatedText) was designed to preserve context across MAX_TOKENS events. - services/iterationUtils.ts had multiple checks intended to catch this: - error_phrase_with_significant_reduction: Should trigger with "Okay, I'm ready..." and >20% loss. (78% loss occurred). - extreme_reduction_error: Should trigger with >75% loss, uninstructed. (78% loss occurred). - hooks/useIterativeLogic.ts was designed to halt processing if isCriticalFailure was true. - **Observed Failure:** The log showed "Iteration 2 completed successfully." This is a critical failure of the validation and error-handling pipeline. - **Minute Detail - Why this Failed (Self-Correction):** - **LLM Context Limit with Massive Prompts:** Even if the continuation prompt is correctly structured, the initial prompt for Iteration 2 (containing the 1.5MB previous product + instructions) is enormous. The LLM might struggle to effectively process such a large initial context before its first MAX_TOKENS event, leading to a breakdown in its understanding of the task, making subsequent continuations less effective. - **Validation Logic Flaw (isLikelyAiErrorResponse):** For Iteration 2 to be marked "successful," isLikelyAiErrorResponse must have returned isError: false, or the isCriticalFailure flag was not set/acted upon correctly in useIterativeLogic.ts. Given the clear presence of an error phrase and extreme reduction, this points to a fundamental flaw in the execution or conditional logic of the validation checks. The order of checks or an unforeseen interaction between conditions likely caused the critical failure to be missed. **Robust Fixing Plan & Proactive Measures:** **Phase 1: Shore Up Critical Defenses (Immediate Implementation)** - **Ironclad Validation (services/iterationUtils.ts):** - **Goal:** Ensure catastrophic failures (like Iteration 2) are ALWAYS caught. - **Actions:** - **Re-Prioritize and Simplify Checks:** - Checks for prompt_leakage, error_phrase_with_significant_reduction, and extreme_reduction_error (uninstructed) will be placed at the very beginning of the function. If any of these critical conditions are met, the function will immediately return isError: true with the corresponding critical checkDetails.type. This prevents them from being masked by less critical checks. - Explicitly verify that foundErrorPhrase combined with a significant uninstructed reduction (newLengthChars < prevLengthChars * SIGNIFICANT_REDUCTION_WITH_ERROR_PHRASE_THRESHOLD) correctly triggers error_phrase_with_significant_reduction. - Explicitly verify that isExtremeReduction (e.g., charPercentageChange < EXTREME_REDUCTION_CHAR_PERCENT_THRESHOLD) when !isInstructedToShorten correctly triggers extreme_reduction_error. - **Refine initial_synthesis_failed_large_output (for Iteration 1):** - **Heuristic 1 (Text-Dominant Input):** If a significant majority (e.g., >80% by count or size) of input files are text-based (mimeType.startsWith('text/')), attempt to estimate total input characters (decode base64 for these files). Then, compare newLengthChars (output) to totalEstimatedInputChars * CHAR_EXPANSION_FACTOR_ITER1 (e.g., 1.5). If output chars exceed this, flag error. - **Heuristic 2 (Mixed/Binary or Fallback):** Use an absolute character limit for Iteration 1 output if input size is below a certain threshold (e.g., if total input bytes < 500KB, then Iteration 1 output > 1,200,000 chars is an error). This acts as a hard cap against extreme verbosity. Combine with the existing byte-to-char factor but make it stricter (e.g., INITIAL_SYNTHESIS_OUTPUT_SIZE_FACTOR_VS_INPUT_BYTES = 2.0). - **Ensure Correct Context:** Verify that currentLogEntryForValidation.fileProcessingInfo.totalFilesSizeBytesSent is accurately reflecting the input for Iteration 1 when its output is validated. - **Guarantee Process Halting (hooks/useIterativeLogic.ts):** - **Goal:** Ensure that a isCriticalFailure from processSingleIteration absolutely stops the main loop and preserves the last known good state. - **Actions:** - Double-check the logic: if (result.isCriticalFailure) { currentProductForIteration = productBeforeThisFailedIteration; finalResultStatus = 'CRITICAL_ERROR_HALT'; processingCompletedSuccessfully = false; break; }. This seems logically sound, so the failure point was likely result.isCriticalFailure not being set due to isLikelyAiErrorResponse failing. The fix in (1) is primary. - The status message for CRITICAL_ERROR_HALT should be very explicit in the UI. - **Strengthen Initial Synthesis Prompt (Iter 1) (services/promptBuilderService.ts):** - **Goal:** Further guide the LLM towards actual synthesis. - **Action:** Add to CRITICAL INITIAL SYNTHESIS: "The primary metric of success for this iteration is the quality of de-duplication and the conciseness of the synthesized output. Producing an output that is excessively long relative to the unique conceptual content of the inputs, or one that contains obvious large-scale redundancies, will be considered a failure to adhere to this critical synthesis task." **Phase 2: Proactive Measures for Enhanced Autonomy & Robustness** - **Smarter Context Management for LLM (services/promptBuilderService.ts):** - **Goal:** Reduce LLM cognitive load when currentProduct becomes excessively large. - **Action (Verify & Refine Existing):** The MAX_PRODUCT_CONTEXT_CHARS_IN_PROMPT (250KB) truncation logic (head/tail excerpt) in buildTextualPromptPart is a good step. - **Refinement:** Ensure the accompanying prompt note to the AI ((Excerpt of ${currentProduct.length} chars - Full document was provided over previous iterations. Focus on refining the overall document based on current instructions and the full context you have accumulated.)) is clear and effectively encourages the AI to rely on its accumulated understanding. - **Multi-Stage Initial Synthesis for Very Large/Complex Inputs (Future Enhancement - Code/LLM Hybrid):** - **Goal:** Break down the initial synthesis task if it's too large for one shot. - **Concept:** - **Step 1 (Outline/Analysis - LLM):** If totalFilesSizeBytesSent > THRESHOLD_FOR_STAGED_SYNTHESIS (e.g., 200KB), the first call for Iteration 1 asks the AI to: "Analyze all provided files. Identify key themes, main sections, versions, and significant redundancies. Output a structured outline of the synthesized document and a list of identified major redundancies/versions. DO NOT generate the full document yet." - **Step 2 (Drafting from Outline - LLM):** The system takes this outline. For actual Iteration 1 product generation, the prompt becomes: "Using the 'Current State of Product' (which is the outline you just generated) and your full knowledge of the original files, generate the full synthesized document. Ensure all redundancies noted in the outline are resolved." - This turns Iteration 1 into an internal two-step process, making the synthesis more manageable for the LLM. This is a significant architectural change. - **Limited Self-Correction Loop (Future Enhancement - Code/LLM Hybrid):** - **Goal:** Allow the system to attempt recovery from some validation failures. - **Concept:** - If isLikelyAiErrorResponse returns isError: true but isCriticalFailure is NOT true (e.g., minor error phrase, non-extreme invalid JSON attempt): - The system could automatically retry the iteration once. - The retry prompt would include: "Your previous response for this iteration had an issue: [aiErrorCheck.reason]. Please try again, specifically addressing this and focusing on the original goal: [original coreUserInstructions for this iteration]." - This adds a layer of resilience without complex state management for full critical failures. **Implementation for This Turn:** I will focus on implementing the crucial fixes from **Phase 1**: - Overhauling services/iterationUtils.ts to make critical validation checks (especially for extreme reduction with stall phrases, and initial synthesis failure) far more robust and correctly prioritized. - Ensuring hooks/useIterativeLogic.ts correctly captures and acts upon critical failures. - Further strengthening the Iteration 1 synthesis prompt in services/promptBuilderService.ts. These changes aim to directly address the root causes of the catastrophic failures observed in the logs.